col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))



#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal)


#only works for melanoma
prim_site_text <- data_frame(PRIMARY_SITE = c("C440",
"C441",
"C442",
"C443",
"C444",
"C445",
"C446",
"C447",
"C448",
"C449", 
"C510",
"C511",
"C512",
"C518",
"C519",
"C600",
"C601",
"C602",
"C608",
"C609",
"C632"),                
SITE_TEXT = c(
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
 
rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

functions

p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")

        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")

        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {

                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))

        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)

        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

Extract Data of Interest

# Melanoma
site_code <- c("C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449", "C510", "C511", "C512", "C518", "C519", "C600", "C601", "C602", "C608", "C609", "C632")
histo_code <- c("8720", "8741", "8746", "8721", "8722", "8723", "8730", "8740", "8742", "8743", "8744", "8745", "8761")

behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(INSURANCE_STATUS %in% c("0", "1", "2", "3", "4"))
   # filter(AGE >= 18) %>%
   #      filter(TNM_CLIN_M %in% c("c0")) %>%
   #      filter(SEQUENCE_NUMBER == "00") %>%
   #      filter(CLASS_OF_CASE %in% c("10", "12", "14", "22")) %>%
        

no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())


file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
save(data,
      file = paste0(file_path, "/melanoma_data.Rda"))
#load("melanoma_data.Rda")

Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

  1. Site codes: C440, C441, C442, C443, C444, C445, C446, C447, C448, C449, C510, C511, C512, C518, C519, C600, C601, C602, C608, C609, C632
  2. Histology codes: 8720, 8741, 8746, 8721, 8722, 8723, 8730, 8740, 8742, 8743, 8744, 8745, 8761
  3. Behavior codes: 3

Patients were excluded if they didn’t have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)
##                              
##                               level Overall       
##   n                                 379702        
##   RX_SUMM_SURG_OTH_REGDIS (%) 0     362910 (95.6) 
##                               1       5561 ( 1.5) 
##                               2       4012 ( 1.1) 
##                               3        951 ( 0.3) 
##                               4       4912 ( 1.3) 
##                               5        880 ( 0.2) 
##                               9        476 ( 0.1)
data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 

Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Table of variables for all cases:

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "SITE_TEXT"))
level Overall
n 362910
FACILITY_TYPE_F (%) Community Cancer Program 20025 ( 5.5)
Comprehensive Comm Ca Program 114916 ( 31.7)
Academic/Research Program 147602 ( 40.7)
Integrated Network Ca Program 38530 ( 10.6)
NA 41837 ( 11.5)
FACILITY_LOCATION_F (%) New England 21270 ( 5.9)
Middle Atlantic 52367 ( 14.4)
South Atlantic 72796 ( 20.1)
East North Central 53630 ( 14.8)
East South Central 21009 ( 5.8)
West North Central 27272 ( 7.5)
West South Central 16025 ( 4.4)
Mountain 17133 ( 4.7)
Pacific 39571 ( 10.9)
NA 41837 ( 11.5)
FACILITY_GEOGRAPHY (%) Northeast 73637 ( 20.3)
South 88821 ( 24.5)
Midwest 101911 ( 28.1)
West 56704 ( 15.6)
NA 41837 ( 11.5)
AGE (mean (sd)) 60.69 (16.46)
AGE_F (%) (0,54] 123853 ( 34.1)
(54,64] 79330 ( 21.9)
(64,74] 76938 ( 21.2)
(74,100] 82755 ( 22.8)
NA 34 ( 0.0)
AGE_40 (%) (0,40] 45430 ( 12.5)
(40,100] 317446 ( 87.5)
NA 34 ( 0.0)
SEX_F (%) Male 207765 ( 57.2)
Female 155145 ( 42.8)
RACE_F (%) White 353647 ( 97.4)
Black 2116 ( 0.6)
Other/Unk 6088 ( 1.7)
Asian 1059 ( 0.3)
HISPANIC (%) No 338276 ( 93.2)
Yes 5052 ( 1.4)
Unknown 19582 ( 5.4)
INSURANCE_F (%) Private 196431 ( 54.1)
None 8903 ( 2.5)
Medicaid 9359 ( 2.6)
Medicare 144437 ( 39.8)
Other Government 3780 ( 1.0)
Unknown 0 ( 0.0)
INCOME_F (%) Less than $38,000 39086 ( 10.8)
$38,000 - $47,999 74009 ( 20.4)
$48,000 - $62,999 98845 ( 27.2)
$63,000 + 149259 ( 41.1)
NA 1711 ( 0.5)
EDUCATION_F (%) 21% or more 36157 ( 10.0)
13 - 20.9% 77301 ( 21.3)
7 - 12.9% 126598 ( 34.9)
Less than 7% 121364 ( 33.4)
NA 1490 ( 0.4)
U_R_F (%) Metro 297888 ( 82.1)
Urban 47786 ( 13.2)
Rural 6199 ( 1.7)
NA 11037 ( 3.0)
CROWFLY (mean (sd)) 32.67 (107.89)
CDCC_TOTAL_BEST (%) 0 313112 ( 86.3)
1 39439 ( 10.9)
2 7684 ( 2.1)
3 2675 ( 0.7)
PRIMARY_SITE (%) C000 0 ( 0.0)
C001 0 ( 0.0)
C002 0 ( 0.0)
C003 0 ( 0.0)
C004 0 ( 0.0)
C005 0 ( 0.0)
C006 0 ( 0.0)
C008 0 ( 0.0)
C009 0 ( 0.0)
C019 0 ( 0.0)
C024 0 ( 0.0)
C029 0 ( 0.0)
C031 0 ( 0.0)
C050 0 ( 0.0)
C051 0 ( 0.0)
C059 0 ( 0.0)
C060 0 ( 0.0)
C069 0 ( 0.0)
C079 0 ( 0.0)
C080 0 ( 0.0)
C089 0 ( 0.0)
C090 0 ( 0.0)
C091 0 ( 0.0)
C098 0 ( 0.0)
C099 0 ( 0.0)
C100 0 ( 0.0)
C108 0 ( 0.0)
C109 0 ( 0.0)
C111 0 ( 0.0)
C113 0 ( 0.0)
C118 0 ( 0.0)
C119 0 ( 0.0)
C129 0 ( 0.0)
C131 0 ( 0.0)
C140 0 ( 0.0)
C142 0 ( 0.0)
C148 0 ( 0.0)
C154 0 ( 0.0)
C159 0 ( 0.0)
C160 0 ( 0.0)
C161 0 ( 0.0)
C162 0 ( 0.0)
C163 0 ( 0.0)
C166 0 ( 0.0)
C168 0 ( 0.0)
C169 0 ( 0.0)
C170 0 ( 0.0)
C171 0 ( 0.0)
C172 0 ( 0.0)
C179 0 ( 0.0)
C180 0 ( 0.0)
C181 0 ( 0.0)
C182 0 ( 0.0)
C184 0 ( 0.0)
C186 0 ( 0.0)
C187 0 ( 0.0)
C188 0 ( 0.0)
C189 0 ( 0.0)
C199 0 ( 0.0)
C209 0 ( 0.0)
C210 0 ( 0.0)
C211 0 ( 0.0)
C218 0 ( 0.0)
C220 0 ( 0.0)
C221 0 ( 0.0)
C239 0 ( 0.0)
C240 0 ( 0.0)
C250 0 ( 0.0)
C252 0 ( 0.0)
C257 0 ( 0.0)
C258 0 ( 0.0)
C259 0 ( 0.0)
C268 0 ( 0.0)
C269 0 ( 0.0)
C300 0 ( 0.0)
C310 0 ( 0.0)
C321 0 ( 0.0)
C339 0 ( 0.0)
C340 0 ( 0.0)
C341 0 ( 0.0)
C342 0 ( 0.0)
C343 0 ( 0.0)
C348 0 ( 0.0)
C349 0 ( 0.0)
C379 0 ( 0.0)
C380 0 ( 0.0)
C381 0 ( 0.0)
C382 0 ( 0.0)
C383 0 ( 0.0)
C384 0 ( 0.0)
C388 0 ( 0.0)
C400 0 ( 0.0)
C402 0 ( 0.0)
C410 0 ( 0.0)
C412 0 ( 0.0)
C413 0 ( 0.0)
C414 0 ( 0.0)
C419 0 ( 0.0)
C420 0 ( 0.0)
C421 0 ( 0.0)
C422 0 ( 0.0)
C424 0 ( 0.0)
C440 631 ( 0.2)
C441 1184 ( 0.3)
C442 10639 ( 2.9)
C443 33680 ( 9.3)
C444 30276 ( 8.3)
C445 113276 ( 31.2)
C446 90064 ( 24.8)
C447 67737 ( 18.7)
C448 389 ( 0.1)
C449 15034 ( 4.1)
C480 0 ( 0.0)
C481 0 ( 0.0)
C482 0 ( 0.0)
C488 0 ( 0.0)
C490 0 ( 0.0)
C491 0 ( 0.0)
C492 0 ( 0.0)
C493 0 ( 0.0)
C494 0 ( 0.0)
C495 0 ( 0.0)
C496 0 ( 0.0)
C499 0 ( 0.0)
C502 0 ( 0.0)
C503 0 ( 0.0)
C504 0 ( 0.0)
C505 0 ( 0.0)
C506 0 ( 0.0)
C508 0 ( 0.0)
C509 0 ( 0.0)
C529 0 ( 0.0)
C559 0 ( 0.0)
C579 0 ( 0.0)
C629 0 ( 0.0)
C649 0 ( 0.0)
C669 0 ( 0.0)
C678 0 ( 0.0)
C679 0 ( 0.0)
C690 0 ( 0.0)
C695 0 ( 0.0)
C696 0 ( 0.0)
C701 0 ( 0.0)
C709 0 ( 0.0)
C710 0 ( 0.0)
C711 0 ( 0.0)
C712 0 ( 0.0)
C713 0 ( 0.0)
C714 0 ( 0.0)
C715 0 ( 0.0)
C716 0 ( 0.0)
C719 0 ( 0.0)
C720 0 ( 0.0)
C729 0 ( 0.0)
C739 0 ( 0.0)
C749 0 ( 0.0)
C760 0 ( 0.0)
C761 0 ( 0.0)
C762 0 ( 0.0)
C763 0 ( 0.0)
C764 0 ( 0.0)
C765 0 ( 0.0)
C770 0 ( 0.0)
C771 0 ( 0.0)
C772 0 ( 0.0)
C773 0 ( 0.0)
C774 0 ( 0.0)
C775 0 ( 0.0)
C778 0 ( 0.0)
C779 0 ( 0.0)
C809 0 ( 0.0)
HISTOLOGY_F_LIM (%) 8720 182616 ( 50.3)
9680 0 ( 0.0)
8743 112470 ( 31.0)
8742 19251 ( 5.3)
Other 48573 ( 13.4)
HISTOLOGY_F (%) 8720 182616 ( 50.3)
9680 0 ( 0.0)
8743 112470 ( 31.0)
8742 19251 ( 5.3)
8721 34314 ( 9.5)
9663 0 ( 0.0)
9690 0 ( 0.0)
9591 0 ( 0.0)
9691 0 ( 0.0)
9650 0 ( 0.0)
9695 0 ( 0.0)
9673 0 ( 0.0)
9699 0 ( 0.0)
8247 0 ( 0.0)
9698 0 ( 0.0)
9823 0 ( 0.0)
9670 0 ( 0.0)
9590 0 ( 0.0)
8070 0 ( 0.0)
9702 0 ( 0.0)
9652 0 ( 0.0)
9687 0 ( 0.0)
8745 5612 ( 1.5)
8832 0 ( 0.0)
8744 4796 ( 1.3)
8772 0 ( 0.0)
9714 0 ( 0.0)
9689 0 ( 0.0)
9659 0 ( 0.0)
9705 0 ( 0.0)
9671 0 ( 0.0)
8410 0 ( 0.0)
9651 0 ( 0.0)
9596 0 ( 0.0)
8730 1330 ( 0.4)
8723 1293 ( 0.4)
9837 0 ( 0.0)
9120 0 ( 0.0)
8830 0 ( 0.0)
8071 0 ( 0.0)
8771 0 ( 0.0)
9665 0 ( 0.0)
8770 0 ( 0.0)
8761 790 ( 0.2)
8409 0 ( 0.0)
9729 0 ( 0.0)
9684 0 ( 0.0)
8890 0 ( 0.0)
9653 0 ( 0.0)
8542 0 ( 0.0)
9667 0 ( 0.0)
8390 0 ( 0.0)
8407 0 ( 0.0)
8413 0 ( 0.0)
9727 0 ( 0.0)
8200 0 ( 0.0)
8740 286 ( 0.1)
8480 0 ( 0.0)
9735 0 ( 0.0)
9679 0 ( 0.0)
8402 0 ( 0.0)
9811 0 ( 0.0)
8140 0 ( 0.0)
9675 0 ( 0.0)
8090 0 ( 0.0)
8802 0 ( 0.0)
8400 0 ( 0.0)
9716 0 ( 0.0)
9827 0 ( 0.0)
8401 0 ( 0.0)
8408 0 ( 0.0)
9719 0 ( 0.0)
9664 0 ( 0.0)
8430 0 ( 0.0)
9728 0 ( 0.0)
8800 0 ( 0.0)
8097 0 ( 0.0)
8833 0 ( 0.0)
8051 0 ( 0.0)
8246 0 ( 0.0)
8801 0 ( 0.0)
8403 0 ( 0.0)
9678 0 ( 0.0)
8560 0 ( 0.0)
8722 91 ( 0.0)
9738 0 ( 0.0)
8010 0 ( 0.0)
8980 0 ( 0.0)
8780 0 ( 0.0)
8525 0 ( 0.0)
8143 0 ( 0.0)
9717 0 ( 0.0)
8940 0 ( 0.0)
8260 0 ( 0.0)
8094 0 ( 0.0)
8746 48 ( 0.0)
8075 0 ( 0.0)
8805 0 ( 0.0)
8811 0 ( 0.0)
8804 0 ( 0.0)
8810 0 ( 0.0)
9655 0 ( 0.0)
8123 0 ( 0.0)
8310 0 ( 0.0)
9709 0 ( 0.0)
8481 0 ( 0.0)
8074 0 ( 0.0)
8982 0 ( 0.0)
8147 0 ( 0.0)
8072 0 ( 0.0)
9737 0 ( 0.0)
9708 0 ( 0.0)
8550 0 ( 0.0)
8420 0 ( 0.0)
8891 0 ( 0.0)
8500 0 ( 0.0)
8083 0 ( 0.0)
8741 13 ( 0.0)
9724 0 ( 0.0)
8076 0 ( 0.0)
8850 0 ( 0.0)
9718 0 ( 0.0)
9700 0 ( 0.0)
8255 0 ( 0.0)
8854 0 ( 0.0)
8081 0 ( 0.0)
8092 0 ( 0.0)
8852 0 ( 0.0)
9540 0 ( 0.0)
8052 0 ( 0.0)
8562 0 ( 0.0)
8711 0 ( 0.0)
9044 0 ( 0.0)
8490 0 ( 0.0)
9580 0 ( 0.0)
9654 0 ( 0.0)
8000 0 ( 0.0)
9133 0 ( 0.0)
8851 0 ( 0.0)
9260 0 ( 0.0)
9701 0 ( 0.0)
8502 0 ( 0.0)
9812 0 ( 0.0)
8032 0 ( 0.0)
8091 0 ( 0.0)
9560 0 ( 0.0)
8440 0 ( 0.0)
8575 0 ( 0.0)
9130 0 ( 0.0)
8406 0 ( 0.0)
8774 0 ( 0.0)
8894 0 ( 0.0)
8910 0 ( 0.0)
8920 0 ( 0.0)
8201 0 ( 0.0)
8240 0 ( 0.0)
8450 0 ( 0.0)
8540 0 ( 0.0)
8574 0 ( 0.0)
8803 0 ( 0.0)
9473 0 ( 0.0)
9597 0 ( 0.0)
9662 0 ( 0.0)
8033 0 ( 0.0)
8073 0 ( 0.0)
8084 0 ( 0.0)
8773 0 ( 0.0)
8815 0 ( 0.0)
8858 0 ( 0.0)
8840 0 ( 0.0)
8900 0 ( 0.0)
8963 0 ( 0.0)
9150 0 ( 0.0)
9180 0 ( 0.0)
9220 0 ( 0.0)
9726 0 ( 0.0)
9814 0 ( 0.0)
9816 0 ( 0.0)
9817 0 ( 0.0)
8050 0 ( 0.0)
8124 0 ( 0.0)
8230 0 ( 0.0)
8249 0 ( 0.0)
8263 0 ( 0.0)
8320 0 ( 0.0)
8323 0 ( 0.0)
8470 0 ( 0.0)
8520 0 ( 0.0)
8573 0 ( 0.0)
8726 0 ( 0.0)
8825 0 ( 0.0)
8896 0 ( 0.0)
9364 0 ( 0.0)
9813 0 ( 0.0)
9815 0 ( 0.0)
8004 0 ( 0.0)
8022 0 ( 0.0)
8078 0 ( 0.0)
8095 0 ( 0.0)
8098 0 ( 0.0)
8190 0 ( 0.0)
8290 0 ( 0.0)
8340 0 ( 0.0)
8341 0 ( 0.0)
8523 0 ( 0.0)
8543 0 ( 0.0)
8710 0 ( 0.0)
8760 0 ( 0.0)
8806 0 ( 0.0)
8835 0 ( 0.0)
9041 0 ( 0.0)
9043 0 ( 0.0)
9170 0 ( 0.0)
9661 0 ( 0.0)
9818 0 ( 0.0)
8020 0 ( 0.0)
8041 0 ( 0.0)
8082 0 ( 0.0)
8102 0 ( 0.0)
8110 0 ( 0.0)
8120 0 ( 0.0)
8121 0 ( 0.0)
8211 0 ( 0.0)
8245 0 ( 0.0)
8270 0 ( 0.0)
8312 0 ( 0.0)
8332 0 ( 0.0)
8347 0 ( 0.0)
8441 0 ( 0.0)
8471 0 ( 0.0)
8503 0 ( 0.0)
8524 0 ( 0.0)
8570 0 ( 0.0)
8583 0 ( 0.0)
8750 0 ( 0.0)
8823 0 ( 0.0)
8831 0 ( 0.0)
8836 0 ( 0.0)
8855 0 ( 0.0)
8936 0 ( 0.0)
8941 0 ( 0.0)
8990 0 ( 0.0)
9000 0 ( 0.0)
9020 0 ( 0.0)
9040 0 ( 0.0)
9080 0 ( 0.0)
9102 0 ( 0.0)
9105 0 ( 0.0)
9451 0 ( 0.0)
9530 0 ( 0.0)
9561 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0)
3 362910 (100.0)
GRADE_F (%) Gr I: Well Diff 759 ( 0.2)
Gr II: Mod Diff 1064 ( 0.3)
Gr III: Poor Diff 1877 ( 0.5)
Gr IV: Undiff/Anaplastic 665 ( 0.2)
5 0 ( 0.0)
6 0 ( 0.0)
7 0 ( 0.0)
8 0 ( 0.0)
NA/Unkown 358545 ( 98.8)
DX_STAGING_PROC_DAYS (mean (sd)) 3.39 (54.84)
TNM_CLIN_T (%) N_A 1608 ( 0.4)
c0 4261 ( 1.2)
c1 18911 ( 5.2)
c1A 55175 ( 15.2)
c1B 20634 ( 5.7)
c2 5403 ( 1.5)
c2A 34068 ( 9.4)
c2B 8717 ( 2.4)
c2C 0 ( 0.0)
c3 3178 ( 0.9)
c3A 13377 ( 3.7)
c3B 10395 ( 2.9)
c4 2170 ( 0.6)
c4A 6281 ( 1.7)
c4B 10655 ( 2.9)
cX 155744 ( 42.9)
pIS 2539 ( 0.7)
NA 9794 ( 2.7)
TNM_CLIN_N (%) N_A 1608 ( 0.4)
c0 247096 ( 68.1)
c1 5666 ( 1.6)
c1A 1325 ( 0.4)
c1B 1624 ( 0.4)
c2 1148 ( 0.3)
c2A 415 ( 0.1)
c2B 805 ( 0.2)
c2C 900 ( 0.2)
c3 2603 ( 0.7)
cX 91422 ( 25.2)
NA 8298 ( 2.3)
TNM_CLIN_M (%) N_A 1609 ( 0.4)
c0 331754 ( 91.4)
c1 4393 ( 1.2)
c1A 1449 ( 0.4)
c1B 1929 ( 0.5)
c1C 5406 ( 1.5)
NA 16370 ( 4.5)
TNM_CLIN_STAGE_GROUP (%) 0 3415 ( 0.9)
1 11136 ( 3.1)
1A 95569 ( 26.3)
1B 63589 ( 17.5)
1C 0 ( 0.0)
2 1978 ( 0.5)
2A 22432 ( 6.2)
2B 15534 ( 4.3)
2C 7948 ( 2.2)
3 11303 ( 3.1)
3A 0 ( 0.0)
3B 0 ( 0.0)
3C 0 ( 0.0)
4 13516 ( 3.7)
4A 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 1609 ( 0.4)
99 114838 ( 31.6)
NA 43 ( 0.0)
TNM_PATH_T (%) N_A 1608 ( 0.4)
p0 4729 ( 1.3)
p1 12061 ( 3.3)
p1A 60788 ( 16.8)
p1B 23363 ( 6.4)
p2 3707 ( 1.0)
p2A 40964 ( 11.3)
p2B 9316 ( 2.6)
p3 2393 ( 0.7)
p3A 17370 ( 4.8)
p3B 12918 ( 3.6)
p4 1628 ( 0.4)
p4A 9301 ( 2.6)
p4B 15277 ( 4.2)
pIS 1358 ( 0.4)
pX 130955 ( 36.1)
NA 15174 ( 4.2)
TNM_PATH_N (%) N_A 1608 ( 0.4)
p0 161766 ( 44.6)
p1 4601 ( 1.3)
p1A 10736 ( 3.0)
p1B 2516 ( 0.7)
p2 1370 ( 0.4)
p2A 3523 ( 1.0)
p2B 1858 ( 0.5)
p2C 1798 ( 0.5)
p3 4889 ( 1.3)
pX 139376 ( 38.4)
NA 28869 ( 8.0)
TNM_PATH_M (%) N_A 1609 ( 0.4)
p1 1989 ( 0.5)
p1A 1165 ( 0.3)
p1B 907 ( 0.2)
p1C 2241 ( 0.6)
pX 155551 ( 42.9)
NA 199448 ( 55.0)
TNM_PATH_STAGE_GROUP (%) 0 2692 ( 0.7)
1 8007 ( 2.2)
1A 86795 ( 23.9)
1B 70294 ( 19.4)
1C 0 ( 0.0)
2 1642 ( 0.5)
2A 24145 ( 6.7)
2B 16599 ( 4.6)
2C 8258 ( 2.3)
3 6964 ( 1.9)
3A 10302 ( 2.8)
3B 9390 ( 2.6)
3C 6606 ( 1.8)
4 7652 ( 2.1)
4A 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 1609 ( 0.4)
99 90809 ( 25.0)
NA 11146 ( 3.1)
DX_RX_STARTED_DAYS (mean (sd)) 11.01 (28.65)
DX_SURG_STARTED_DAYS (mean (sd)) 10.38 (27.55)
DX_DEFSURG_STARTED_DAYS (mean (sd)) 31.34 (35.58)
MARGINS (%) No Residual 329265 ( 90.7)
Residual, NOS 5326 ( 1.5)
Microscopic Resid 5723 ( 1.6)
Macroscopic Resid 366 ( 0.1)
Not evaluable 999 ( 0.3)
No surg 16733 ( 4.6)
Unknown 4498 ( 1.2)
MARGINS_YN (%) No 329265 ( 90.7)
Yes 11415 ( 3.1)
No surg/Unk/NA 22230 ( 6.1)
SURG_DISCHARGE_DAYS (mean (sd)) 1.74 (9.14)
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 347736 ( 95.8)
Unplan_Readmit_Same 3407 ( 0.9)
Plan_Readmit_Same 5388 ( 1.5)
PlanUnplan_Same 515 ( 0.1)
9 5864 ( 1.6)
RX_SUMM_RADIATION_F (%) None 349080 ( 96.2)
Beam Radiation 11458 ( 3.2)
Radioactive Implants 44 ( 0.0)
Radioisotopes 9 ( 0.0)
Beam + Imp or Isotopes 9 ( 0.0)
Radiation, NOS 114 ( 0.0)
Unknown 2196 ( 0.6)
PUF_30_DAY_MORT_CD_F (%) Alive_30 337013 ( 92.9)
Dead_30 792 ( 0.2)
Unknown 8120 ( 2.2)
NA 16985 ( 4.7)
PUF_90_DAY_MORT_CD_F (%) Alive_90 329604 ( 90.8)
Dead_90 2646 ( 0.7)
Unknown 13675 ( 3.8)
NA 16985 ( 4.7)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 55.24 (41.17)
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 145065 ( 40.0)
Pos_LumphVasc_Inv 7694 ( 2.1)
N_A 50 ( 0.0)
Unknown 50201 ( 13.8)
NA 159900 ( 44.1)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 18689 ( 5.1)
Robot_Assist 148 ( 0.0)
Robot_to_Open 37 ( 0.0)
Endo_Lap 441 ( 0.1)
Endo_Lap_to_Open 170 ( 0.0)
Open_Unknown 183492 ( 50.6)
Unknown 33 ( 0.0)
NA 159900 ( 44.1)
SURG_RAD_SEQ (%) Surg Alone 337579 ( 93.0)
Surg then Rad 6249 ( 1.7)
Rad Alone 5240 ( 1.4)
No Treatment 10837 ( 3.0)
Other 2891 ( 0.8)
Rad before and after Surg 17 ( 0.0)
Rad then Surg 97 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 324878 ( 89.5)
No Surg, No Rad, Yes Chemo 2269 ( 0.6)
No Surg, No Rad, No Chemo 8215 ( 2.3)
Surg, No rad, Yes Chemo 4027 ( 1.1)
Other 12228 ( 3.4)
Rad, No Surg, Yes Chemo 1859 ( 0.5)
Surg then Rad, No Chemo 5156 ( 1.4)
Surg then Rad, Yes Chemo 906 ( 0.2)
Rad, No Surg, No Chemo 3258 ( 0.9)
Rad then Surg, No Chemo 66 ( 0.0)
Rad then Surg, Yes Chemo 31 ( 0.0)
Rad before and after Surg, No Chemo 14 ( 0.0)
Rad before and after Surg, Yes Chemo 3 ( 0.0)
SURGERY_YN (%) No 16214 ( 4.5)
Ukn 684 ( 0.2)
Yes 346012 ( 95.3)
RADIATION_YN (%) No 348906 ( 96.1)
Yes 11634 ( 3.2)
NA 2370 ( 0.7)
CHEMO_YN (%) No 342669 ( 94.4)
Yes 9216 ( 2.5)
Ukn 11025 ( 3.0)
mets_at_dx (%) Bone 763 ( 0.2)
Brain 886 ( 0.2)
Liver 592 ( 0.2)
Lung 3474 ( 1.0)
None/Other/Unk/NA 357195 ( 98.4)
MEDICAID_EXPN_CODE (%) Non-Expansion State 114831 ( 31.6)
Jan 2014 Expansion States 100097 ( 27.6)
Early Expansion States (2010-13) 61288 ( 16.9)
Late Expansion States (> Jan 2014) 44857 ( 12.4)
Suppressed for Ages 0 - 39 41837 ( 11.5)
EXPN_GROUP (%) Exclude 41837 ( 11.5)
Post-Expansion 55764 ( 15.4)
Pre-Expansion 265309 ( 73.1)
SITE_TEXT (%) C44.0 Skin of lip, NOS 631 ( 0.2)
C44.1 Eyelid 1184 ( 0.3)
C44.2 External ear 10639 ( 2.9)
C44.3 Skin of ear and unspecified parts of face 33680 ( 9.3)
C44.4 Skin of scalp and neck 30276 ( 8.3)
C44.5 Skin of trunk 113276 ( 31.2)
C44.6 Skin of upper limb and shoulder 90064 ( 24.8)
C44.7 Skin of lower limb and hip 67737 ( 18.7)
C44.8 Overlapping lesion of skin 389 ( 0.1)
C44.9 Skin, NOS 15034 ( 4.1)
p_table(no_Excludes,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "PRIMARY_SITE", "HISTOLOGY_F_LIM", "HISTOLOGY_F", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE","SITE_TEXT"), 
        strata = "EXPN_GROUP")
level Post-Expansion Pre-Expansion p test
n 57862 278211
FACILITY_TYPE_F (%) Community Cancer Program 3221 ( 5.6) 17611 ( 6.3) <0.001
Comprehensive Comm Ca Program 19026 ( 32.9) 100775 ( 36.2)
Academic/Research Program 31135 ( 53.8) 123481 ( 44.4)
Integrated Network Ca Program 4480 ( 7.7) 36344 ( 13.1)
FACILITY_LOCATION_F (%) New England 5930 ( 10.2) 16209 ( 5.8) <0.001
Middle Atlantic 12096 ( 20.9) 42501 ( 15.3)
South Atlantic 2410 ( 4.2) 74171 ( 26.7)
East North Central 5811 ( 10.0) 50106 ( 18.0)
East South Central 1343 ( 2.3) 21004 ( 7.5)
West North Central 7194 ( 12.4) 21268 ( 7.6)
West South Central 127 ( 0.2) 16744 ( 6.0)
Mountain 2479 ( 4.3) 15535 ( 5.6)
Pacific 20472 ( 35.4) 20673 ( 7.4)
FACILITY_GEOGRAPHY (%) Northeast 18026 ( 31.2) 58710 ( 21.1) <0.001
South 2537 ( 4.4) 90915 ( 32.7)
Midwest 14348 ( 24.8) 92378 ( 33.2)
West 22951 ( 39.7) 36208 ( 13.0)
AGE (mean (sd)) 65.49 (12.79) 64.34 (13.03) <0.001
AGE_F (%) (0,54] 12889 ( 22.3) 73067 ( 26.3) <0.001
(54,64] 14728 ( 25.5) 68624 ( 24.7)
(64,74] 14815 ( 25.6) 65791 ( 23.6)
(74,100] 15430 ( 26.7) 70729 ( 25.4)
AGE_40 (%) (0,40] 509 ( 0.9) 3282 ( 1.2) <0.001
(40,100] 57353 ( 99.1) 274929 ( 98.8)
SEX_F (%) Male 34722 ( 60.0) 167707 ( 60.3) 0.225
Female 23140 ( 40.0) 110504 ( 39.7)
RACE_F (%) White 56383 ( 97.4) 271444 ( 97.6) <0.001
Black 248 ( 0.4) 1767 ( 0.6)
Other/Unk 940 ( 1.6) 4399 ( 1.6)
Asian 291 ( 0.5) 601 ( 0.2)
HISPANIC (%) No 55363 ( 95.7) 258455 ( 92.9) <0.001
Yes 1091 ( 1.9) 3305 ( 1.2)
Unknown 1408 ( 2.4) 16451 ( 5.9)
INSURANCE_F (%) Private 28513 ( 49.3) 139177 ( 50.0) <0.001
None 728 ( 1.3) 6660 ( 2.4)
Medicaid 1938 ( 3.3) 5338 ( 1.9)
Medicare 26274 ( 45.4) 124013 ( 44.6)
Other Government 409 ( 0.7) 3023 ( 1.1)
INCOME_F (%) Less than $38,000 3143 ( 5.4) 33541 ( 12.1) <0.001
$38,000 - $47,999 7627 ( 13.2) 61337 ( 22.0)
$48,000 - $62,999 14132 ( 24.4) 77116 ( 27.7)
$63,000 + 32828 ( 56.7) 104761 ( 37.7)
NA 132 ( 0.2) 1456 ( 0.5)
EDUCATION_F (%) 21% or more 4586 ( 7.9) 29264 ( 10.5) <0.001
13 - 20.9% 9266 ( 16.0) 62785 ( 22.6)
7 - 12.9% 20331 ( 35.1) 96700 ( 34.8)
Less than 7% 23572 ( 40.7) 88186 ( 31.7)
NA 107 ( 0.2) 1276 ( 0.5)
U_R_F (%) Metro 49795 ( 86.1) 225331 ( 81.0) <0.001
Urban 5798 ( 10.0) 38929 ( 14.0)
Rural 534 ( 0.9) 5387 ( 1.9)
NA 1735 ( 3.0) 8564 ( 3.1)
CROWFLY (mean (sd)) 29.28 (105.80) 33.49 (107.35) <0.001
CDCC_TOTAL_BEST (%) 0 48500 ( 83.8) 236220 ( 84.9) <0.001
1 7282 ( 12.6) 33004 ( 11.9)
2 1468 ( 2.5) 6708 ( 2.4)
3 612 ( 1.1) 2279 ( 0.8)
PRIMARY_SITE (%) C440 95 ( 0.2) 510 ( 0.2) 0.001
C441 181 ( 0.3) 975 ( 0.4)
C442 1682 ( 2.9) 8426 ( 3.0)
C443 5412 ( 9.4) 27343 ( 9.8)
C444 4881 ( 8.4) 23580 ( 8.5)
C445 17330 ( 30.0) 82506 ( 29.7)
C446 14455 ( 25.0) 68861 ( 24.8)
C447 10302 ( 17.8) 48320 ( 17.4)
C448 64 ( 0.1) 318 ( 0.1)
C449 3460 ( 6.0) 17372 ( 6.2)
HISTOLOGY_F_LIM (%) 8720 30919 ( 53.4) 141506 ( 50.9) <0.001
8743 16201 ( 28.0) 81437 ( 29.3)
8742 3050 ( 5.3) 16238 ( 5.8)
Other 7692 ( 13.3) 39030 ( 14.0)
HISTOLOGY_F (%) 8720 30919 ( 53.4) 141506 ( 50.9) <0.001
8743 16201 ( 28.0) 81437 ( 29.3)
8742 3050 ( 5.3) 16238 ( 5.8)
8721 5413 ( 9.4) 27541 ( 9.9)
8745 942 ( 1.6) 4665 ( 1.7)
8744 757 ( 1.3) 3871 ( 1.4)
8730 273 ( 0.5) 1124 ( 0.4)
8723 151 ( 0.3) 1042 ( 0.4)
8761 91 ( 0.2) 469 ( 0.2)
8740 41 ( 0.1) 195 ( 0.1)
8722 12 ( 0.0) 74 ( 0.0)
8746 9 ( 0.0) 39 ( 0.0)
8741 3 ( 0.0) 10 ( 0.0)
BEHAVIOR (%) 3 57862 (100.0) 278211 (100.0) NA
GRADE_F (%) Gr I: Well Diff 88 ( 0.2) 606 ( 0.2) <0.001
Gr II: Mod Diff 139 ( 0.2) 804 ( 0.3)
Gr III: Poor Diff 245 ( 0.4) 1688 ( 0.6)
Gr IV: Undiff/Anaplastic 127 ( 0.2) 536 ( 0.2)
NA/Unkown 57263 ( 99.0) 274577 ( 98.7)
DX_STAGING_PROC_DAYS (mean (sd)) 2.43 (22.43) 3.98 (61.22) 0.001
TNM_CLIN_T (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
c0 1231 ( 2.1) 4801 ( 1.7)
c1 3089 ( 5.3) 13742 ( 4.9)
c1A 16108 ( 27.8) 33019 ( 11.9)
c1B 5554 ( 9.6) 12635 ( 4.5)
c2 925 ( 1.6) 4089 ( 1.5)
c2A 6115 ( 10.6) 24936 ( 9.0)
c2B 1727 ( 3.0) 6602 ( 2.4)
c3 531 ( 0.9) 2536 ( 0.9)
c3A 2494 ( 4.3) 9998 ( 3.6)
c3B 2067 ( 3.6) 8069 ( 2.9)
c4 385 ( 0.7) 1768 ( 0.6)
c4A 1120 ( 1.9) 4984 ( 1.8)
c4B 2061 ( 3.6) 8534 ( 3.1)
cX 10139 ( 17.5) 133420 ( 48.0)
pIS 563 ( 1.0) 1902 ( 0.7)
NA 3749 ( 6.5) 5631 ( 2.0)
TNM_CLIN_N (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
c0 46329 ( 80.1) 178425 ( 64.1)
c1 1298 ( 2.2) 4595 ( 1.7)
c1A 142 ( 0.2) 1125 ( 0.4)
c1B 216 ( 0.4) 1479 ( 0.5)
c2 229 ( 0.4) 941 ( 0.3)
c2A 59 ( 0.1) 362 ( 0.1)
c2B 111 ( 0.2) 757 ( 0.3)
c2C 207 ( 0.4) 820 ( 0.3)
c3 543 ( 0.9) 2380 ( 0.9)
cX 5486 ( 9.5) 81017 ( 29.1)
NA 3238 ( 5.6) 4765 ( 1.7)
TNM_CLIN_M (%) N_A 4 ( 0.0) 1546 ( 0.6) <0.001
c0 48596 ( 84.0) 253240 ( 91.0)
c1 660 ( 1.1) 4964 ( 1.8)
c1A 382 ( 0.7) 1666 ( 0.6)
c1B 511 ( 0.9) 1937 ( 0.7)
c1C 1652 ( 2.9) 5358 ( 1.9)
NA 6057 ( 10.5) 9500 ( 3.4)
TNM_CLIN_STAGE_GROUP (%) 0 727 ( 1.3) 2588 ( 0.9) <0.001
1 1843 ( 3.2) 7997 ( 2.9)
1A 17346 ( 30.0) 66257 ( 23.8)
1B 11641 ( 20.1) 45327 ( 16.3)
2 306 ( 0.5) 1529 ( 0.5)
2A 4183 ( 7.2) 16896 ( 6.1)
2B 2900 ( 5.0) 12121 ( 4.4)
2C 1554 ( 2.7) 6284 ( 2.3)
3 2074 ( 3.6) 9546 ( 3.4)
4 3324 ( 5.7) 14210 ( 5.1)
N_A 4 ( 0.0) 1545 ( 0.6)
99 11944 ( 20.6) 93881 ( 33.7)
NA 16 ( 0.0) 30 ( 0.0)
TNM_PATH_T (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
p0 1085 ( 1.9) 3953 ( 1.4)
p1 1747 ( 3.0) 8963 ( 3.2)
p1A 18169 ( 31.4) 36049 ( 13.0)
p1B 6406 ( 11.1) 14138 ( 5.1)
p2 469 ( 0.8) 2936 ( 1.1)
p2A 7761 ( 13.4) 29413 ( 10.6)
p2B 1824 ( 3.2) 6995 ( 2.5)
p3 345 ( 0.6) 1970 ( 0.7)
p3A 3365 ( 5.8) 12866 ( 4.6)
p3B 2637 ( 4.6) 9921 ( 3.6)
p4 263 ( 0.5) 1384 ( 0.5)
p4A 1905 ( 3.3) 7222 ( 2.6)
p4B 3249 ( 5.6) 12057 ( 4.3)
pIS 247 ( 0.4) 1064 ( 0.4)
pX 3576 ( 6.2) 117143 ( 42.1)
NA 4810 ( 8.3) 10592 ( 3.8)
TNM_PATH_N (%) N_A 4 ( 0.0) 1545 ( 0.6) <0.001
p0 28616 ( 49.5) 117080 ( 42.1)
p1 765 ( 1.3) 3785 ( 1.4)
p1A 2157 ( 3.7) 7757 ( 2.8)
p1B 522 ( 0.9) 2050 ( 0.7)
p2 194 ( 0.3) 1150 ( 0.4)
p2A 697 ( 1.2) 2511 ( 0.9)
p2B 373 ( 0.6) 1547 ( 0.6)
p2C 411 ( 0.7) 1545 ( 0.6)
p3 1013 ( 1.8) 4283 ( 1.5)
pX 14289 ( 24.7) 115799 ( 41.6)
NA 8821 ( 15.2) 19159 ( 6.9)
TNM_PATH_M (%) N_A 4 ( 0.0) 1546 ( 0.6) <0.001
p1 234 ( 0.4) 2673 ( 1.0)
p1A 278 ( 0.5) 1488 ( 0.5)
p1B 267 ( 0.5) 1103 ( 0.4)
p1C 756 ( 1.3) 2799 ( 1.0)
pX 0 ( 0.0) 139768 ( 50.2)
NA 56323 ( 97.3) 128834 ( 46.3)
TNM_PATH_STAGE_GROUP (%) 0 533 ( 0.9) 1952 ( 0.7) <0.001
1 1169 ( 2.0) 5820 ( 2.1)
1A 13915 ( 24.0) 61591 ( 22.1)
1B 11287 ( 19.5) 51180 ( 18.4)
2 199 ( 0.3) 1313 ( 0.5)
2A 3925 ( 6.8) 18647 ( 6.7)
2B 2861 ( 4.9) 13085 ( 4.7)
2C 1501 ( 2.6) 6636 ( 2.4)
3 753 ( 1.3) 5995 ( 2.2)
3A 1850 ( 3.2) 7526 ( 2.7)
3B 1697 ( 2.9) 7347 ( 2.6)
3C 1346 ( 2.3) 5452 ( 2.0)
4 1845 ( 3.2) 9302 ( 3.3)
N_A 4 ( 0.0) 1545 ( 0.6)
99 11871 ( 20.5) 73084 ( 26.3)
NA 3106 ( 5.4) 7736 ( 2.8)
DX_RX_STARTED_DAYS (mean (sd)) 12.90 (27.62) 11.19 (29.78) <0.001
DX_SURG_STARTED_DAYS (mean (sd)) 12.21 (26.69) 10.62 (28.92) <0.001
DX_DEFSURG_STARTED_DAYS (mean (sd)) 34.09 (33.12) 31.01 (36.44) <0.001
MARGINS (%) No Residual 51064 ( 88.3) 245712 ( 88.3) <0.001
Residual, NOS 866 ( 1.5) 4434 ( 1.6)
Microscopic Resid 1152 ( 2.0) 4515 ( 1.6)
Macroscopic Resid 57 ( 0.1) 316 ( 0.1)
Not evaluable 168 ( 0.3) 839 ( 0.3)
No surg 3970 ( 6.9) 18608 ( 6.7)
Unknown 585 ( 1.0) 3787 ( 1.4)
MARGINS_YN (%) No 51064 ( 88.3) 245712 ( 88.3) 0.003
Yes 2075 ( 3.6) 9265 ( 3.3)
No surg/Unk/NA 4723 ( 8.2) 23234 ( 8.4)
SURG_DISCHARGE_DAYS (mean (sd)) 0.78 (5.84) 2.02 (9.89) <0.001
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 56685 ( 98.0) 264998 ( 95.3) <0.001
Unplan_Readmit_Same 304 ( 0.5) 2988 ( 1.1)
Plan_Readmit_Same 572 ( 1.0) 4583 ( 1.6)
PlanUnplan_Same 68 ( 0.1) 419 ( 0.2)
9 233 ( 0.4) 5223 ( 1.9)
RX_SUMM_RADIATION_F (%) None 55406 ( 95.8) 264311 ( 95.0) <0.001
Beam Radiation 2316 ( 4.0) 11767 ( 4.2)
Radioactive Implants 6 ( 0.0) 47 ( 0.0)
Radioisotopes 1 ( 0.0) 9 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 9 ( 0.0)
Radiation, NOS 18 ( 0.0) 119 ( 0.0)
Unknown 115 ( 0.2) 1949 ( 0.7)
PUF_30_DAY_MORT_CD_F (%) Alive_30 52152 ( 90.1) 253347 ( 91.1) <0.001
Dead_30 117 ( 0.2) 684 ( 0.2)
Unknown 1574 ( 2.7) 5350 ( 1.9)
NA 4019 ( 6.9) 18830 ( 6.8)
PUF_90_DAY_MORT_CD_F (%) Alive_90 50589 ( 87.4) 248035 ( 89.2) <0.001
Dead_90 394 ( 0.7) 2345 ( 0.8)
Unknown 2860 ( 4.9) 9001 ( 3.2)
NA 4019 ( 6.9) 18830 ( 6.8)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 31.33 (21.63) 58.15 (42.18) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 40047 ( 69.2) 93220 ( 33.5) <0.001
Pos_LumphVasc_Inv 2138 ( 3.7) 5456 ( 2.0)
N_A 12 ( 0.0) 39 ( 0.0)
Unknown 15665 ( 27.1) 34140 ( 12.3)
NA 0 ( 0.0) 145356 ( 52.2)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 6404 ( 11.1) 15420 ( 5.5) <0.001
Robot_Assist 53 ( 0.1) 85 ( 0.0)
Robot_to_Open 8 ( 0.0) 25 ( 0.0)
Endo_Lap 93 ( 0.2) 320 ( 0.1)
Endo_Lap_to_Open 28 ( 0.0) 140 ( 0.1)
Open_Unknown 51264 ( 88.6) 116844 ( 42.0)
Unknown 12 ( 0.0) 21 ( 0.0)
NA 0 ( 0.0) 145356 ( 52.2)
SURG_RAD_SEQ (%) Surg Alone 52746 ( 91.2) 251930 ( 90.6) <0.001
Surg then Rad 1385 ( 2.4) 7626 ( 2.7)
Rad Alone 900 ( 1.6) 4034 ( 1.4)
No Treatment 2574 ( 4.4) 11690 ( 4.2)
Other 209 ( 0.4) 2675 ( 1.0)
Rad before and after Surg 6 ( 0.0) 32 ( 0.0)
Rad then Surg 42 ( 0.1) 224 ( 0.1)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 51057 ( 88.2) 241895 ( 86.9) <0.001
No Surg, No Rad, Yes Chemo 460 ( 0.8) 2306 ( 0.8)
No Surg, No Rad, No Chemo 2036 ( 3.5) 8957 ( 3.2)
Surg, No rad, Yes Chemo 478 ( 0.8) 3333 ( 1.2)
Other 1552 ( 2.7) 10121 ( 3.6)
Rad, No Surg, Yes Chemo 272 ( 0.5) 1432 ( 0.5)
Surg then Rad, No Chemo 1113 ( 1.9) 5921 ( 2.1)
Surg then Rad, Yes Chemo 240 ( 0.4) 1488 ( 0.5)
Rad, No Surg, No Chemo 606 ( 1.0) 2507 ( 0.9)
Rad then Surg, No Chemo 28 ( 0.0) 134 ( 0.0)
Rad then Surg, Yes Chemo 14 ( 0.0) 85 ( 0.0)
Rad before and after Surg, No Chemo 5 ( 0.0) 22 ( 0.0)
Rad before and after Surg, Yes Chemo 1 ( 0.0) 10 ( 0.0)
SURGERY_YN (%) No 3935 ( 6.8) 18037 ( 6.5) <0.001
Ukn 76 ( 0.1) 718 ( 0.3)
Yes 53851 ( 93.1) 259456 ( 93.3)
RADIATION_YN (%) No 55371 ( 95.7) 264124 ( 94.9) <0.001
Yes 2341 ( 4.0) 11951 ( 4.3)
NA 150 ( 0.3) 2136 ( 0.8)
CHEMO_YN (%) No 54956 ( 95.0) 260473 ( 93.6) <0.001
Yes 1475 ( 2.5) 8777 ( 3.2)
Ukn 1431 ( 2.5) 8961 ( 3.2)
mets_at_dx (%) Bone 236 ( 0.4) 647 ( 0.2) <0.001
Brain 425 ( 0.7) 1109 ( 0.4)
Liver 173 ( 0.3) 455 ( 0.2)
Lung 1296 ( 2.2) 3085 ( 1.1)
None/Other/Unk/NA 55732 ( 96.3) 272915 ( 98.1)
MEDICAID_EXPN_CODE (%) Non-Expansion State 0 ( 0.0) 121024 ( 43.5) <0.001
Jan 2014 Expansion States 21534 ( 37.2) 83126 ( 29.9)
Early Expansion States (2010-13) 36328 ( 62.8) 27157 ( 9.8)
Late Expansion States (> Jan 2014) 0 ( 0.0) 46904 ( 16.9)
SITE_TEXT (%) C44.0 Skin of lip, NOS 95 ( 0.2) 510 ( 0.2) 0.001
C44.1 Eyelid 181 ( 0.3) 975 ( 0.4)
C44.2 External ear 1682 ( 2.9) 8426 ( 3.0)
C44.3 Skin of ear and unspecified parts of face 5412 ( 9.4) 27343 ( 9.8)
C44.4 Skin of scalp and neck 4881 ( 8.4) 23580 ( 8.5)
C44.5 Skin of trunk 17330 ( 30.0) 82506 ( 29.7)
C44.6 Skin of upper limb and shoulder 14455 ( 25.0) 68861 ( 24.8)
C44.7 Skin of lower limb and hip 10302 ( 17.8) 48320 ( 17.4)
C44.8 Overlapping lesion of skin 64 ( 0.1) 318 ( 0.1)
C44.9 Skin, NOS 3460 ( 6.0) 17372 ( 6.2)
p_table(data,
        vars = c("YEAR_OF_DIAGNOSIS"),
        strata = c("MEDICAID_EXPN_CODE"))
level Non-Expansion State Jan 2014 Expansion States Early Expansion States (2010-13) Late Expansion States (> Jan 2014) Suppressed for Ages 0 - 39 p test
n 114831 100097 61288 44857 41837
YEAR_OF_DIAGNOSIS (%) 2004 7215 ( 6.3) 6291 ( 6.3) 3790 ( 6.2) 2684 ( 6.0) 3361 (8.0) NaN
2005 7840 ( 6.8) 6855 ( 6.8) 4054 ( 6.6) 2982 ( 6.6) 3669 (8.8)
2006 7954 ( 6.9) 7319 ( 7.3) 4202 ( 6.9) 2954 ( 6.6) 3550 (8.5)
2007 8545 ( 7.4) 7439 ( 7.4) 4550 ( 7.4) 3082 ( 6.9) 3585 (8.6)
2008 8944 ( 7.8) 7931 ( 7.9) 4698 ( 7.7) 3287 ( 7.3) 3493 (8.3)
2009 9456 ( 8.2) 8000 ( 8.0) 4906 ( 8.0) 3682 ( 8.2) 3582 (8.6)
2010 9553 ( 8.3) 8261 ( 8.3) 5015 ( 8.2) 3772 ( 8.4) 3367 (8.0)
2011 9941 ( 8.7) 8794 ( 8.8) 5369 ( 8.8) 3948 ( 8.8) 3293 (7.9)
2012 10449 ( 9.1) 8965 ( 9.0) 5489 ( 9.0) 4097 ( 9.1) 3394 (8.1)
2013 11143 ( 9.7) 9566 ( 9.6) 6101 (10.0) 4390 ( 9.8) 3425 (8.2)
2014 11455 (10.0) 10065 (10.1) 6319 (10.3) 4823 (10.8) 3507 (8.4)
2015 12336 (10.7) 10611 (10.6) 6795 (11.1) 5156 (11.5) 3611 (8.6)
2016 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) 0 (0.0)
preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))

# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))
## 
##  2-sample test for equality of proportions with continuity
##  correction
## 
## data:  c(preExpMedicare, postExpMedicare) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpMedicare, postExpMedicare) out of     filter(EXPN_GROUP == "Post-Expansion")))
## X-squared = 10.134, df = 1, p-value = 0.001455
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.011941128 -0.002829993
## sample estimates:
##    prop 1    prop 2 
## 0.4465812 0.4539667
preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))

# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))
## 
##  2-sample test for equality of proportions with continuity
##  correction
## 
## data:  c(preExpNoInsurance, postExpNoInsurance) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpNoInsurance, postExpNoInsurance) out of     filter(EXPN_GROUP == "Post-Expansion")))
## X-squared = 262.61, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  0.009865323 0.012057405
## sample estimates:
##     prop 1     prop 2 
## 0.02340667 0.01244531
p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")
level Post-Expansion Pre-Expansion p test
n 57862 278211
DX_RX_STARTED_DAYS (mean (sd)) 12.90 (27.62) 11.19 (29.78) <0.001
data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

Kaplan Meier Analysis

All

uni_var(test_var = "All", data_imp = data)
## _________________________________________________
##    
## ## All
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ All, data = data)
## 
##       n  events  median 0.95LCL 0.95UCL 
##  362910   89972     164     162     165 
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ All, data = data)
## 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 304157   21947    0.936 0.000417        0.935        0.937
##    24 261059   17623    0.880 0.000569        0.879        0.881
##    36 216079   13651    0.831 0.000675        0.829        0.832
##    48 178134    9949    0.790 0.000757        0.788        0.791
##    60 144302    7410    0.754 0.000828        0.752        0.756
##   120  34572   17189    0.613 0.001249        0.610        0.615
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  All
## 
## [1] "Only one level, no Cox model performed"
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  All

Facility Type

uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)

## _________________________________________________
##    
## ## FACILITY_TYPE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_TYPE_F, data = data)
## 
##    41837 observations deleted due to missingness 
##                                                    n events median 0.95LCL
## FACILITY_TYPE_F=Community Cancer Program       20025   6627    126     122
## FACILITY_TYPE_F=Comprehensive Comm Ca Program 114916  34156    144     142
## FACILITY_TYPE_F=Academic/Research Program     147602  35238    162     158
## FACILITY_TYPE_F=Integrated Network Ca Program  38530  10808    147     143
##                                               0.95UCL
## FACILITY_TYPE_F=Community Cancer Program          132
## FACILITY_TYPE_F=Comprehensive Comm Ca Program     146
## FACILITY_TYPE_F=Academic/Research Program          NA
## FACILITY_TYPE_F=Integrated Network Ca Program     152
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_TYPE_F, data = data)
## 
## 41837 observations deleted due to missingness 
##                 FACILITY_TYPE_F=Community Cancer Program 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  16242    1969    0.897 0.00221        0.892        0.901
##    24  13658    1293    0.823 0.00283        0.817        0.828
##    36  11147     946    0.762 0.00323        0.756        0.768
##    48   9167     668    0.713 0.00354        0.707        0.720
##    60   7352     502    0.671 0.00380        0.664        0.679
##   120   1706    1106    0.515 0.00535        0.504        0.525
## 
##                 FACILITY_TYPE_F=Comprehensive Comm Ca Program 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  95753    8748    0.920 0.000818        0.919        0.922
##    24  81903    6518    0.855 0.001087        0.853        0.857
##    36  67972    4953    0.800 0.001267        0.798        0.803
##    48  56271    3728    0.754 0.001406        0.751        0.756
##    60  46023    2747    0.714 0.001522        0.711        0.717
##   120  11044    6556    0.558 0.002195        0.553        0.562
## 
##                 FACILITY_TYPE_F=Academic/Research Program 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 123953    7681    0.945 0.000613        0.944        0.946
##    24 105844    7140    0.888 0.000870        0.886        0.890
##    36  86436    5624    0.837 0.001051        0.835        0.839
##    48  70096    4059    0.795 0.001191        0.793        0.797
##    60  55804    3049    0.757 0.001317        0.755        0.760
##   120  12528    6853    0.606 0.002069        0.602        0.610
## 
##                 FACILITY_TYPE_F=Integrated Network Ca Program 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  32077    2792    0.924 0.00139        0.921        0.927
##    24  27452    2016    0.863 0.00184        0.860        0.867
##    36  22620    1625    0.809 0.00216        0.805        0.813
##    48  18519    1144    0.765 0.00240        0.760        0.770
##    60  14948     840    0.727 0.00261        0.722        0.733
##   120   3461    2119    0.567 0.00391        0.559        0.575
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  FACILITY_TYPE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_TYPE_F, data = data)
## 
##   n= 321073, number of events= 86829 
##    (41837 observations deleted due to missingness)
## 
##                                                  coef exp(coef) se(coef)
## FACILITY_TYPE_FComprehensive Comm Ca Program -0.16443   0.84837  0.01342
## FACILITY_TYPE_FAcademic/Research Program     -0.35814   0.69898  0.01339
## FACILITY_TYPE_FIntegrated Network Ca Program -0.20659   0.81336  0.01560
##                                                   z Pr(>|z|)    
## FACILITY_TYPE_FComprehensive Comm Ca Program -12.25   <2e-16 ***
## FACILITY_TYPE_FAcademic/Research Program     -26.75   <2e-16 ***
## FACILITY_TYPE_FIntegrated Network Ca Program -13.24   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                              exp(coef) exp(-coef)
## FACILITY_TYPE_FComprehensive Comm Ca Program    0.8484      1.179
## FACILITY_TYPE_FAcademic/Research Program        0.6990      1.431
## FACILITY_TYPE_FIntegrated Network Ca Program    0.8134      1.229
##                                              lower .95 upper .95
## FACILITY_TYPE_FComprehensive Comm Ca Program    0.8263    0.8710
## FACILITY_TYPE_FAcademic/Research Program        0.6809    0.7176
## FACILITY_TYPE_FIntegrated Network Ca Program    0.7889    0.8386
## 
## Concordance= 0.535  (se = 0.001 )
## Rsquare= 0.003   (max possible= 0.999 )
## Likelihood ratio test= 1066  on 3 df,   p=0
## Wald test            = 1079  on 3 df,   p=0
## Score (logrank) test = 1085  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_TYPE_F

Facility Location

uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)

## _________________________________________________
##    
## ## FACILITY_LOCATION_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_LOCATION_F, data = data)
## 
##    41837 observations deleted due to missingness 
##                                            n events median 0.95LCL 0.95UCL
## FACILITY_LOCATION_F=New England        21270   5637    147     144     156
## FACILITY_LOCATION_F=Middle Atlantic    52367  13264    154     150     157
## FACILITY_LOCATION_F=South Atlantic     72796  20410    144     142     148
## FACILITY_LOCATION_F=East North Central 53630  14618    151     149     155
## FACILITY_LOCATION_F=East South Central 21009   6492    136     131     140
## FACILITY_LOCATION_F=West North Central 27272   6879    157     151      NA
## FACILITY_LOCATION_F=West South Central 16025   4815    146     139     153
## FACILITY_LOCATION_F=Mountain           17133   4508    153     148     161
## FACILITY_LOCATION_F=Pacific            39571  10206     NA      NA      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_LOCATION_F, data = data)
## 
## 41837 observations deleted due to missingness 
##                 FACILITY_LOCATION_F=New England 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  17981    1277    0.937 0.00171        0.934        0.940
##    24  15509    1065    0.879 0.00235        0.875        0.884
##    36  12846     877    0.826 0.00281        0.821        0.832
##    48  10594     601    0.785 0.00313        0.779        0.791
##    60   8434     492    0.745 0.00345        0.739        0.752
##   120   1929    1149    0.581 0.00537        0.571        0.592
## 
##                 FACILITY_LOCATION_F=Middle Atlantic 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  44645    3196    0.936 0.00110        0.934        0.938
##    24  37929    2675    0.877 0.00150        0.874        0.880
##    36  30808    2040    0.827 0.00179        0.823        0.830
##    48  24591    1549    0.781 0.00203        0.778        0.785
##    60  19240    1080    0.744 0.00223        0.739        0.748
##   120   3694    2426    0.586 0.00363        0.578        0.593
## 
##                 FACILITY_LOCATION_F=South Atlantic 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  60080    4784    0.931 0.00097        0.929        0.932
##    24  51140    3921    0.867 0.00133        0.865        0.870
##    36  42412    3076    0.812 0.00158        0.809        0.815
##    48  34971    2301    0.765 0.00176        0.761        0.768
##    60  28464    1687    0.725 0.00192        0.721        0.729
##   120   6770    4105    0.564 0.00283        0.558        0.569
## 
##                 FACILITY_LOCATION_F=East North Central 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  44891    3581    0.930 0.00113        0.928        0.932
##    24  38367    2827    0.869 0.00153        0.866        0.872
##    36  31453    2229    0.815 0.00181        0.811        0.819
##    48  25729    1592    0.771 0.00203        0.767        0.775
##    60  20748    1195    0.732 0.00222        0.728        0.736
##   120   4963    2833    0.577 0.00328        0.571        0.584
## 
##                 FACILITY_LOCATION_F=East South Central 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  17689    1576    0.922 0.00190        0.918        0.925
##    24  14959    1312    0.851 0.00257        0.846        0.856
##    36  12339     969    0.792 0.00300        0.786        0.798
##    48  10142     722    0.743 0.00333        0.736        0.749
##    60   8140     527    0.701 0.00361        0.694        0.708
##   120   1873    1217    0.537 0.00528        0.526        0.547
## 
##                 FACILITY_LOCATION_F=West North Central 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  22526    1684    0.934 0.00155        0.931        0.937
##    24  19360    1304    0.878 0.00210        0.874        0.882
##    36  15791    1092    0.825 0.00251        0.820        0.830
##    48  12855     751    0.783 0.00282        0.777        0.788
##    60  10288     580    0.744 0.00310        0.738        0.750
##   120   2307    1297    0.594 0.00476        0.585        0.603
## 
##                 FACILITY_LOCATION_F=West South Central 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  13328    1417    0.908 0.00234        0.903        0.912
##    24  11177     962    0.839 0.00303        0.833        0.845
##    36   9198     685    0.784 0.00348        0.778        0.791
##    48   7559     501    0.739 0.00384        0.731        0.746
##    60   6099     389    0.698 0.00415        0.690        0.706
##   120   1195     781    0.552 0.00610        0.540        0.564
## 
##                 FACILITY_LOCATION_F=Mountain 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  14210    1161    0.928 0.00202        0.925        0.932
##    24  12205     861    0.870 0.00271        0.865        0.875
##    36  10058     666    0.819 0.00318        0.813        0.826
##    48   8267     494    0.776 0.00356        0.769        0.783
##    60   6834     341    0.742 0.00386        0.734        0.749
##   120   1644     849    0.593 0.00586        0.582        0.605
## 
##                 FACILITY_LOCATION_F=Pacific 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  32675    2514    0.933 0.00129        0.931        0.936
##    24  28211    2040    0.873 0.00177        0.869        0.876
##    36  23270    1514    0.822 0.00209        0.818        0.827
##    48  19345    1088    0.781 0.00233        0.777        0.786
##    60  15880     847    0.744 0.00254        0.740        0.749
##   120   4364    1977    0.606 0.00364        0.599        0.613
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  FACILITY_LOCATION_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_LOCATION_F, data = data)
## 
##   n= 321073, number of events= 86829 
##    (41837 observations deleted due to missingness)
## 
##                                             coef  exp(coef)   se(coef)
## FACILITY_LOCATION_FMiddle Atlantic    -0.0018090  0.9981926  0.0159011
## FACILITY_LOCATION_FSouth Atlantic      0.0745691  1.0774198  0.0150466
## FACILITY_LOCATION_FEast North Central  0.0430144  1.0439529  0.0156783
## FACILITY_LOCATION_FEast South Central  0.1684812  1.1835059  0.0182054
## FACILITY_LOCATION_FWest North Central -0.0144924  0.9856121  0.0179659
## FACILITY_LOCATION_FWest South Central  0.1737202  1.1897227  0.0196242
## FACILITY_LOCATION_FMountain           -0.0008789  0.9991215  0.0199809
## FACILITY_LOCATION_FPacific            -0.0363362  0.9643160  0.0165960
##                                            z Pr(>|z|)    
## FACILITY_LOCATION_FMiddle Atlantic    -0.114  0.90942    
## FACILITY_LOCATION_FSouth Atlantic      4.956  7.2e-07 ***
## FACILITY_LOCATION_FEast North Central  2.744  0.00608 ** 
## FACILITY_LOCATION_FEast South Central  9.254  < 2e-16 ***
## FACILITY_LOCATION_FWest North Central -0.807  0.41986    
## FACILITY_LOCATION_FWest South Central  8.852  < 2e-16 ***
## FACILITY_LOCATION_FMountain           -0.044  0.96492    
## FACILITY_LOCATION_FPacific            -2.189  0.02856 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                       exp(coef) exp(-coef) lower .95
## FACILITY_LOCATION_FMiddle Atlantic       0.9982     1.0018    0.9676
## FACILITY_LOCATION_FSouth Atlantic        1.0774     0.9281    1.0461
## FACILITY_LOCATION_FEast North Central    1.0440     0.9579    1.0124
## FACILITY_LOCATION_FEast South Central    1.1835     0.8449    1.1420
## FACILITY_LOCATION_FWest North Central    0.9856     1.0146    0.9515
## FACILITY_LOCATION_FWest South Central    1.1897     0.8405    1.1448
## FACILITY_LOCATION_FMountain              0.9991     1.0009    0.9608
## FACILITY_LOCATION_FPacific               0.9643     1.0370    0.9335
##                                       upper .95
## FACILITY_LOCATION_FMiddle Atlantic       1.0298
## FACILITY_LOCATION_FSouth Atlantic        1.1097
## FACILITY_LOCATION_FEast North Central    1.0765
## FACILITY_LOCATION_FEast South Central    1.2265
## FACILITY_LOCATION_FWest North Central    1.0209
## FACILITY_LOCATION_FWest South Central    1.2364
## FACILITY_LOCATION_FMountain              1.0390
## FACILITY_LOCATION_FPacific               0.9962
## 
## Concordance= 0.517  (se = 0.001 )
## Rsquare= 0.001   (max possible= 0.999 )
## Likelihood ratio test= 331.4  on 8 df,   p=0
## Wald test            = 337.3  on 8 df,   p=0
## Score (logrank) test = 337.9  on 8 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_LOCATION_F

Facility Geography

uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)

## _________________________________________________
##    
## ## FACILITY_GEOGRAPHY
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_GEOGRAPHY, data = data)
## 
##    41837 observations deleted due to missingness 
##                                   n events median 0.95LCL 0.95UCL
## FACILITY_GEOGRAPHY=Northeast  73637  18901    153     147     156
## FACILITY_GEOGRAPHY=South      88821  25225    144     142     148
## FACILITY_GEOGRAPHY=Midwest   101911  27989    149     146     151
## FACILITY_GEOGRAPHY=West       56704  14714     NA     161      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_GEOGRAPHY, data = data)
## 
## 41837 observations deleted due to missingness 
##                 FACILITY_GEOGRAPHY=Northeast 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  62626    4473    0.936 0.000923        0.934        0.938
##    24  53438    3740    0.878 0.001266        0.876        0.880
##    36  43654    2917    0.827 0.001508        0.824        0.830
##    48  35185    2150    0.783 0.001702        0.779        0.786
##    60  27674    1572    0.744 0.001875        0.741        0.748
##   120   5623    3575    0.584 0.003008        0.578        0.590
## 
##                 FACILITY_GEOGRAPHY=South 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  73408    6201    0.926 0.000901        0.925        0.928
##    24  62317    4883    0.862 0.001222        0.860        0.864
##    36  51610    3761    0.807 0.001438        0.804        0.810
##    48  42530    2802    0.760 0.001604        0.757        0.763
##    60  34563    2076    0.720 0.001743        0.717        0.724
##   120   7965    4886    0.561 0.002568        0.556        0.566
## 
##                 FACILITY_GEOGRAPHY=Midwest 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  85106    6841    0.929 0.000825        0.928        0.931
##    24  72686    5443    0.868 0.001118        0.865        0.870
##    36  59583    4290    0.813 0.001323        0.810        0.815
##    48  48726    3065    0.768 0.001478        0.765        0.771
##    60  39176    2302    0.729 0.001615        0.725        0.732
##   120   9143    5347    0.573 0.002409        0.568        0.578
## 
##                 FACILITY_GEOGRAPHY=West 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  46885    3675    0.932 0.00109        0.930        0.934
##    24  40416    2901    0.872 0.00148        0.869        0.875
##    36  33328    2180    0.822 0.00175        0.818        0.825
##    48  27612    1582    0.780 0.00195        0.776        0.784
##    60  22714    1188    0.744 0.00212        0.740        0.748
##   120   6008    2826    0.602 0.00309        0.596        0.608
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  FACILITY_GEOGRAPHY
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ FACILITY_GEOGRAPHY, data = data)
## 
##   n= 321073, number of events= 86829 
##    (41837 observations deleted due to missingness)
## 
##                                coef exp(coef)  se(coef)      z Pr(>|z|)
## FACILITY_GEOGRAPHYSouth    0.094031  1.098594  0.009622  9.772  < 2e-16
## FACILITY_GEOGRAPHYMidwest  0.057178  1.058844  0.009416  6.072 1.26e-09
## FACILITY_GEOGRAPHYWest    -0.024315  0.975978  0.010999 -2.211   0.0271
##                              
## FACILITY_GEOGRAPHYSouth   ***
## FACILITY_GEOGRAPHYMidwest ***
## FACILITY_GEOGRAPHYWest    *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                           exp(coef) exp(-coef) lower .95 upper .95
## FACILITY_GEOGRAPHYSouth       1.099     0.9103    1.0781    1.1195
## FACILITY_GEOGRAPHYMidwest     1.059     0.9444    1.0395    1.0786
## FACILITY_GEOGRAPHYWest        0.976     1.0246    0.9552    0.9972
## 
## Concordance= 0.512  (se = 0.001 )
## Rsquare= 0.001   (max possible= 0.999 )
## Likelihood ratio test= 173.3  on 3 df,   p=0
## Wald test            = 172.8  on 3 df,   p=0
## Score (logrank) test = 172.9  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_GEOGRAPHY

Age Group

uni_var(test_var = "AGE_F", data_imp = data)

## _________________________________________________
##    
## ## AGE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_F, data = data)
## 
##    34 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## AGE_F=(0,54]   123853  13264     NA      NA      NA
## AGE_F=(54,64]   79330  13732     NA      NA      NA
## AGE_F=(64,74]   76938  20008  139.4   138.0   141.9
## AGE_F=(74,100]  82755  42964   59.7    59.1    60.4
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_F, data = data)
## 
## 34 observations deleted due to missingness 
##                 AGE_F=(0,54] 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 106374    3468    0.970 0.000498        0.969        0.971
##    24  94556    2739    0.944 0.000690        0.943        0.946
##    36  81723    2031    0.923 0.000823        0.921        0.924
##    48  70239    1454    0.905 0.000927        0.903        0.907
##    60  59008    1047    0.891 0.001015        0.889        0.893
##   120  16859    2249    0.839 0.001483        0.836        0.842
## 
##                 AGE_F=(54,64] 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  66772    3653    0.951 0.000786        0.950        0.953
##    24  58185    2649    0.912 0.001064        0.910        0.914
##    36  48718    2102    0.877 0.001271        0.874        0.879
##    48  40530    1456    0.849 0.001429        0.846        0.851
##    60  33238    1023    0.825 0.001564        0.822        0.828
##   120   8217    2499    0.725 0.002443        0.721        0.730
## 
##                 AGE_F=(64,74] 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  64339    4647    0.936 0.000904        0.935        0.938
##    24  55056    3600    0.882 0.001228        0.879        0.884
##    36  44903    2968    0.831 0.001472        0.828        0.834
##    48  36459    2189    0.787 0.001666        0.784        0.790
##    60  28999    1618    0.749 0.001837        0.745        0.752
##   120   6181    4296    0.565 0.003012        0.559        0.571
## 
##                 AGE_F=(74,100] 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  66647   10178    0.872 0.00119        0.870        0.874
##    24  53243    8634    0.755 0.00156        0.752        0.758
##    36  40718    6549    0.656 0.00177        0.653        0.660
##    48  30892    4850    0.572 0.00191        0.569        0.576
##    60  23046    3721    0.498 0.00202        0.494        0.502
##   120   3310    8145    0.229 0.00247        0.225        0.234
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  AGE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_F, data = data)
## 
##   n= 362876, number of events= 89968 
##    (34 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)    
## AGE_F(54,64]  0.570757  1.769606 0.012178  46.87   <2e-16 ***
## AGE_F(64,74]  1.044519  2.842031 0.011211  93.17   <2e-16 ***
## AGE_F(74,100] 1.915597  6.790994 0.009999 191.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## AGE_F(54,64]      1.770     0.5651     1.728     1.812
## AGE_F(64,74]      2.842     0.3519     2.780     2.905
## AGE_F(74,100]     6.791     0.1473     6.659     6.925
## 
## Concordance= 0.68  (se = 0.001 )
## Rsquare= 0.125   (max possible= 0.998 )
## Likelihood ratio test= 48267  on 3 df,   p=0
## Wald test            = 46114  on 3 df,   p=0
## Score (logrank) test = 56187  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_F

Age Group

uni_var(test_var = "AGE_40", data_imp = data)

## _________________________________________________
##    
## ## AGE_40
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_40, data = data)
## 
##    34 observations deleted due to missingness 
##                      n events median 0.95LCL 0.95UCL
## AGE_40=(0,40]    45430   3484     NA      NA      NA
## AGE_40=(40,100] 317446  86484    150     149     152
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_40, data = data)
## 
## 34 observations deleted due to missingness 
##                 AGE_40=(0,40] 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  39270     846    0.980 0.000677        0.979        0.981
##    24  34993     735    0.961 0.000965        0.959        0.963
##    36  30341     544    0.945 0.001165        0.943        0.947
##    48  26165     400    0.932 0.001325        0.929        0.934
##    60  21941     292    0.921 0.001462        0.918        0.923
##   120   6350     612    0.881 0.002175        0.877        0.885
## 
##                 AGE_40=(40,100] 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 264862   21100    0.930 0.000465        0.929        0.931
##    24 226047   16887    0.868 0.000632        0.867        0.870
##    36 185721   13106    0.815 0.000748        0.813        0.816
##    48 151955    9549    0.770 0.000836        0.768        0.771
##    60 122350    7117    0.731 0.000914        0.729        0.732
##   120  28217   16577    0.574 0.001372        0.572        0.577
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  AGE_40
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ AGE_40, data = data)
## 
##   n= 362876, number of events= 89968 
##    (34 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)     z Pr(>|z|)    
## AGE_40(40,100] 1.41156   4.10235  0.01729 81.66   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## AGE_40(40,100]     4.102     0.2438     3.966     4.244
## 
## Concordance= 0.548  (se = 0.001 )
## Rsquare= 0.029   (max possible= 0.998 )
## Likelihood ratio test= 10562  on 1 df,   p=0
## Wald test            = 6669  on 1 df,   p=0
## Score (logrank) test = 7851  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_40

Gender

uni_var(test_var = "SEX_F", data_imp = data)

## _________________________________________________
##    
## ## SEX_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SEX_F, data = data)
## 
##                   n events median 0.95LCL 0.95UCL
## SEX_F=Male   207765  60900    140     139     142
## SEX_F=Female 155145  29072     NA      NA      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SEX_F, data = data)
## 
##                 SEX_F=Male 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 172446   14990    0.924 0.000597        0.923        0.925
##    24 146109   12112    0.856 0.000810        0.855        0.858
##    36 119424    9256    0.799 0.000954        0.797        0.800
##    48  97169    6775    0.750 0.001063        0.748        0.752
##    60  77841    5036    0.708 0.001158        0.706        0.710
##   120  17787   11370    0.546 0.001703        0.542        0.549
## 
##                 SEX_F=Female 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 131711    6957    0.953 0.000555        0.952        0.954
##    24 114950    5511    0.911 0.000763        0.910        0.913
##    36  96655    4395    0.874 0.000915        0.872        0.876
##    48  80965    3174    0.843 0.001033        0.841        0.845
##    60  66461    2374    0.816 0.001138        0.814        0.819
##   120  16785    5819    0.703 0.001777        0.700        0.707
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  SEX_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SEX_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                  coef exp(coef)  se(coef)      z Pr(>|z|)    
## SEX_FFemale -0.527427  0.590122  0.007132 -73.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##             exp(coef) exp(-coef) lower .95 upper .95
## SEX_FFemale    0.5901      1.695    0.5819    0.5984
## 
## Concordance= 0.561  (se = 0.001 )
## Rsquare= 0.016   (max possible= 0.998 )
## Likelihood ratio test= 5762  on 1 df,   p=0
## Wald test            = 5469  on 1 df,   p=0
## Score (logrank) test = 5597  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  SEX_F

RACE_F

uni_var(test_var = "RACE_F", data_imp = data)

## _________________________________________________
##    
## ## RACE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RACE_F, data = data)
## 
##                       n events median 0.95LCL 0.95UCL
## RACE_F=White     353647  87552  164.4   161.8     165
## RACE_F=Black       2116    907   73.1    65.5      84
## RACE_F=Other/Unk   6088   1213     NA      NA      NA
## RACE_F=Asian       1059    300     NA   141.8      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RACE_F, data = data)
## 
##                 RACE_F=White 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 296701   21227    0.937 0.000421        0.936        0.938
##    24 254717   17131    0.880 0.000575        0.879        0.882
##    36 210851   13290    0.831 0.000683        0.830        0.833
##    48 173829    9704    0.790 0.000766        0.789        0.792
##    60 140792    7222    0.755 0.000838        0.753        0.756
##   120  33608   16816    0.613 0.001267        0.610        0.615
## 
##                 RACE_F=Black 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1583     341    0.831 0.00835        0.815        0.848
##    24   1291     170    0.739 0.00999        0.720        0.759
##    36   1016     125    0.663 0.01105        0.641        0.685
##    48    789      89    0.601 0.01182        0.578        0.624
##    60    601      68    0.545 0.01251        0.521        0.570
##   120    132     102    0.409 0.01570        0.379        0.441
## 
##                 RACE_F=Other/Unk 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5045     278    0.951 0.00288        0.945        0.956
##    24   4376     251    0.901 0.00409        0.893        0.909
##    36   3680     188    0.860 0.00489        0.850        0.870
##    48   3110     123    0.829 0.00545        0.819        0.840
##    60   2594     103    0.800 0.00598        0.788        0.812
##   120    749     245    0.689 0.00864        0.672        0.706
## 
##                 RACE_F=Asian 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    828     101    0.898 0.00962        0.880        0.917
##    24    675      71    0.817 0.01270        0.793        0.842
##    36    532      48    0.754 0.01465        0.726        0.783
##    48    406      33    0.702 0.01620        0.671        0.734
##    60    315      17    0.670 0.01723        0.637        0.704
##   120     83      26    0.585 0.02227        0.543        0.630
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  RACE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RACE_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                     coef exp(coef) se(coef)      z Pr(>|z|)    
## RACE_FBlack      0.75331   2.12402  0.03338 22.569  < 2e-16 ***
## RACE_FOther/Unk -0.25173   0.77745  0.02891 -8.707  < 2e-16 ***
## RACE_FAsian      0.28409   1.32855  0.05783  4.912 9.01e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## RACE_FBlack        2.1240     0.4708    1.9895    2.2676
## RACE_FOther/Unk    0.7775     1.2862    0.7346    0.8228
## RACE_FAsian        1.3285     0.7527    1.1862    1.4880
## 
## Concordance= 0.505  (se = 0 )
## Rsquare= 0.001   (max possible= 0.998 )
## Likelihood ratio test= 511.8  on 3 df,   p=0
## Wald test            = 613.3  on 3 df,   p=0
## Score (logrank) test = 639  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  RACE_F

Hispanic

uni_var(test_var = "HISPANIC", data_imp = data)

## _________________________________________________
##    
## ## HISPANIC
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISPANIC, data = data)
## 
##                       n events median 0.95LCL 0.95UCL
## HISPANIC=No      338276  83282    163     162     165
## HISPANIC=Yes       5052   1341     NA     145      NA
## HISPANIC=Unknown  19582   5349     NA      NA      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISPANIC, data = data)
## 
##                 HISPANIC=No 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 283450   20334    0.937 0.000431        0.936        0.937
##    24 242887   16388    0.880 0.000589        0.879        0.881
##    36 200359   12724    0.831 0.000700        0.830        0.832
##    48 164465    9264    0.790 0.000786        0.788        0.791
##    60 132607    6841    0.754 0.000861        0.752        0.756
##   120  31127   15746    0.612 0.001309        0.609        0.614
## 
##                 HISPANIC=Yes 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4050     420    0.912 0.00413        0.903        0.920
##    24   3338     303    0.840 0.00549        0.829        0.851
##    36   2681     199    0.786 0.00634        0.773        0.798
##    48   2162     134    0.743 0.00698        0.730        0.757
##    60   1709      90    0.709 0.00753        0.695        0.724
##   120    362     174    0.595 0.01064        0.575        0.616
## 
##                 HISPANIC=Unknown 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  16657    1193    0.936 0.00179        0.933        0.940
##    24  14834     932    0.882 0.00241        0.878        0.887
##    36  13039     728    0.837 0.00280        0.832        0.843
##    48  11507     551    0.800 0.00309        0.794        0.806
##    60   9986     479    0.765 0.00334        0.759        0.772
##   120   3083    1269    0.629 0.00458        0.620        0.638
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  HISPANIC
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISPANIC, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                     coef exp(coef) se(coef)      z Pr(>|z|)    
## HISPANICYes      0.17257   1.18835  0.02753  6.269 3.64e-10 ***
## HISPANICUnknown -0.05147   0.94983  0.01412 -3.646 0.000266 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## HISPANICYes        1.1883     0.8415    1.1259    1.2542
## HISPANICUnknown    0.9498     1.0528    0.9239    0.9765
## 
## Concordance= 0.503  (se = 0 )
## Rsquare= 0   (max possible= 0.998 )
## Likelihood ratio test= 52.05  on 2 df,   p=4.972e-12
## Wald test            = 54.04  on 2 df,   p=1.84e-12
## Score (logrank) test = 54.15  on 2 df,   p=1.741e-12
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  HISPANIC

Insurance Status

uni_var(test_var = "INSURANCE_F", data_imp = data)

## _________________________________________________
##    
## ## INSURANCE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ INSURANCE_F, data = data)
## 
##                                   n events median 0.95LCL 0.95UCL
## INSURANCE_F=Private          196431  26154     NA      NA      NA
## INSURANCE_F=None               8903   2462  165.2     165      NA
## INSURANCE_F=Medicaid           9359   2921  139.3     127   150.7
## INSURANCE_F=Medicare         144437  57598   86.8      86    87.6
## INSURANCE_F=Other Government   3780    837  160.3     160      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ INSURANCE_F, data = data)
## 
##                 INSURANCE_F=Private 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 168550    5841    0.968 0.000407        0.968        0.969
##    24 149287    5097    0.938 0.000577        0.937        0.939
##    36 127672    4149    0.910 0.000703        0.909        0.912
##    48 108390    2942    0.888 0.000798        0.886        0.889
##    60  90243    2240    0.868 0.000883        0.866        0.870
##   120  24426    5180    0.790 0.001370        0.787        0.792
## 
##                 INSURANCE_F=None 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7020     885    0.895 0.00335        0.888        0.901
##    24   5936     498    0.829 0.00422        0.820        0.837
##    36   4958     357    0.776 0.00478        0.767        0.786
##    48   4101     250    0.735 0.00520        0.724        0.745
##    60   3264     141    0.707 0.00550        0.696        0.718
##   120    777     302    0.604 0.00757        0.589        0.619
## 
##                 INSURANCE_F=Medicaid 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7089    1207    0.863 0.00366        0.856        0.871
##    24   5580     624    0.783 0.00452        0.774        0.792
##    36   4214     372    0.726 0.00508        0.716        0.736
##    48   3298     240    0.680 0.00555        0.669        0.691
##    60   2551     145    0.647 0.00592        0.636        0.659
##   120    552     301    0.528 0.00837        0.512        0.544
## 
##                 INSURANCE_F=Medicare 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 118458   13738    0.901 0.000806        0.899        0.902
##    24  97750   11209    0.812 0.001077        0.810        0.814
##    36  77222    8653    0.735 0.001253        0.733        0.738
##    48  60748    6438    0.669 0.001385        0.667        0.672
##    60  46997    4839    0.611 0.001496        0.608        0.614
##   120   8575   11291    0.379 0.002129        0.375        0.383
## 
##                 INSURANCE_F=Other Government 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3040     276    0.922 0.00453        0.913        0.931
##    24   2506     195    0.859 0.00605        0.847        0.871
##    36   2013     120    0.815 0.00698        0.801        0.828
##    48   1597      79    0.780 0.00771        0.765        0.795
##    60   1247      45    0.755 0.00828        0.739        0.772
##   120    242     115    0.637 0.01336        0.611        0.663
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  INSURANCE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ INSURANCE_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                 coef exp(coef) se(coef)      z Pr(>|z|)
## INSURANCE_FNone             0.897310  2.452997 0.021084  42.56   <2e-16
## INSURANCE_FMedicaid         1.179156  3.251627 0.019523  60.40   <2e-16
## INSURANCE_FMedicare         1.313677  3.719828 0.007489 175.40   <2e-16
## INSURANCE_FOther Government 0.727012  2.068889 0.035119  20.70   <2e-16
## INSURANCE_FUnknown                NA        NA 0.000000     NA       NA
##                                
## INSURANCE_FNone             ***
## INSURANCE_FMedicaid         ***
## INSURANCE_FMedicare         ***
## INSURANCE_FOther Government ***
## INSURANCE_FUnknown             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                             exp(coef) exp(-coef) lower .95 upper .95
## INSURANCE_FNone                 2.453     0.4077     2.354     2.556
## INSURANCE_FMedicaid             3.252     0.3075     3.130     3.378
## INSURANCE_FMedicare             3.720     0.2688     3.666     3.775
## INSURANCE_FOther Government     2.069     0.4834     1.931     2.216
## INSURANCE_FUnknown                 NA         NA        NA        NA
## 
## Concordance= 0.647  (se = 0.001 )
## Rsquare= 0.091   (max possible= 0.998 )
## Likelihood ratio test= 34566  on 4 df,   p=0
## Wald test            = 31004  on 4 df,   p=0
## Score (logrank) test = 35429  on 4 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  INSURANCE_F

Overall Survival pre/post-ACA expansion

uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)

## _________________________________________________
##    
## ## EXPN_GROUP
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EXPN_GROUP, data = no_Excludes)
## 
##                                n events median 0.95LCL 0.95UCL
## EXPN_GROUP=Post-Expansion  57862  10046     NA      NA      NA
## EXPN_GROUP=Pre-Expansion  278211  84641    147     146     149
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EXPN_GROUP, data = no_Excludes)
## 
##                 EXPN_GROUP=Post-Expansion 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  45462    3817    0.929 0.00111        0.927        0.931
##    24  33837    2728    0.868 0.00154        0.865        0.871
##    36  20120    1681    0.815 0.00192        0.811        0.818
##    48  11866     896    0.769 0.00236        0.764        0.773
##    60   7412     497    0.729 0.00283        0.724        0.735
## 
##                 EXPN_GROUP=Pre-Expansion 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 233209   20815    0.922 0.000522        0.921        0.923
##    24 202961   16126    0.856 0.000695        0.855        0.857
##    36 174188   12427    0.801 0.000806        0.800        0.803
##    48 147051    9219    0.757 0.000885        0.755        0.758
##    60 120495    6991    0.718 0.000953        0.716        0.720
##   120  29573   16837    0.566 0.001353        0.563        0.568
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  EXPN_GROUP
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EXPN_GROUP, data = no_Excludes)
## 
##   n= 336073, number of events= 94687 
## 
##                            coef exp(coef) se(coef)     z Pr(>|z|)    
## EXPN_GROUPPre-Expansion 0.06250   1.06449  0.01072 5.828 5.61e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## EXPN_GROUPPre-Expansion     1.064     0.9394     1.042     1.087
## 
## Concordance= 0.504  (se = 0.001 )
## Rsquare= 0   (max possible= 0.999 )
## Likelihood ratio test= 34.49  on 1 df,   p=4.286e-09
## Wald test            = 33.97  on 1 df,   p=5.607e-09
## Score (logrank) test = 33.98  on 1 df,   p=5.576e-09
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  EXPN_GROUP

Education

uni_var(test_var = "EDUCATION_F", data_imp = data)

## _________________________________________________
##    
## ## EDUCATION_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EDUCATION_F, data = data)
## 
##    1490 observations deleted due to missingness 
##                               n events median 0.95LCL 0.95UCL
## EDUCATION_F=21% or more   36157  11317    139     134     142
## EDUCATION_F=13 - 20.9%    77301  21594    151     147     154
## EDUCATION_F=7 - 12.9%    126598  31601    162     161      NA
## EDUCATION_F=Less than 7% 121364  25137     NA      NA      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EDUCATION_F, data = data)
## 
## 1490 observations deleted due to missingness 
##                 EDUCATION_F=21% or more 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  29746    3036    0.912 0.00153        0.909        0.915
##    24  25060    2349    0.837 0.00204        0.833        0.841
##    36  20505    1695    0.777 0.00236        0.772        0.782
##    48  16928    1209    0.728 0.00260        0.723        0.733
##    60  13674     884    0.687 0.00279        0.682        0.693
##   120   3213    1892    0.540 0.00392        0.533        0.548
## 
##                 EDUCATION_F=13 - 20.9% 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  64315    5467    0.926 0.000969        0.924        0.927
##    24  54669    4263    0.862 0.001306        0.859        0.864
##    36  44993    3213    0.808 0.001533        0.805        0.811
##    48  36957    2354    0.763 0.001708        0.759        0.766
##    60  29767    1743    0.724 0.001859        0.720        0.727
##   120   7011    4059    0.570 0.002748        0.564        0.575
## 
##                 EDUCATION_F=7 - 12.9% 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 106148    7706    0.936 0.000708        0.934        0.937
##    24  90972    6119    0.880 0.000964        0.878        0.881
##    36  74962    4856    0.829 0.001148        0.827        0.832
##    48  61570    3532    0.787 0.001289        0.785        0.790
##    60  49939    2620    0.751 0.001412        0.748        0.754
##   120  12025    5994    0.609 0.002122        0.605        0.613
## 
##                 EDUCATION_F=Less than 7% 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 102819    5667    0.951 0.000640        0.949        0.952
##    24  89468    4825    0.904 0.000893        0.902        0.906
##    36  74880    3837    0.863 0.001073        0.861        0.865
##    48  62086    2815    0.828 0.001215        0.826        0.830
##    60  50461    2131    0.797 0.001342        0.794        0.800
##   120  12233    5190    0.667 0.002110        0.663        0.671
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  EDUCATION_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ EDUCATION_F, data = data)
## 
##   n= 361420, number of events= 89649 
##    (1490 observations deleted due to missingness)
## 
##                             coef exp(coef) se(coef)      z Pr(>|z|)    
## EDUCATION_F13 - 20.9%   -0.13004   0.87806  0.01160 -11.21   <2e-16 ***
## EDUCATION_F7 - 12.9%    -0.26039   0.77075  0.01095 -23.77   <2e-16 ***
## EDUCATION_FLess than 7% -0.47909   0.61935  0.01132 -42.32   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## EDUCATION_F13 - 20.9%      0.8781      1.139    0.8583    0.8983
## EDUCATION_F7 - 12.9%       0.7707      1.297    0.7544    0.7875
## EDUCATION_FLess than 7%    0.6193      1.615    0.6058    0.6332
## 
## Concordance= 0.548  (se = 0.001 )
## Rsquare= 0.006   (max possible= 0.998 )
## Likelihood ratio test= 2342  on 3 df,   p=0
## Wald test            = 2335  on 3 df,   p=0
## Score (logrank) test = 2360  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  EDUCATION_F

Urban/Rural

uni_var(test_var = "U_R_F", data_imp = data)

## _________________________________________________
##    
## ## U_R_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ U_R_F, data = data)
## 
##    11037 observations deleted due to missingness 
##                  n events median 0.95LCL 0.95UCL
## U_R_F=Metro 297888  72541    165     164      NA
## U_R_F=Urban  47786  12979    153     148     157
## U_R_F=Rural   6199   1825    141     134     149
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ U_R_F, data = data)
## 
## 11037 observations deleted due to missingness 
##                 U_R_F=Metro 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 249758   17698    0.937 0.000456        0.936        0.938
##    24 214853   14140    0.882 0.000623        0.881        0.883
##    36 178176   10974    0.834 0.000739        0.833        0.835
##    48 147112    8058    0.794 0.000830        0.792        0.795
##    60 119251    6004    0.759 0.000909        0.757        0.760
##   120  28714   13883    0.620 0.001369        0.617        0.623
## 
##                 U_R_F=Urban 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  40132    3196    0.930 0.00120        0.927        0.932
##    24  34165    2546    0.868 0.00163        0.865        0.871
##    36  28024    1986    0.814 0.00192        0.811        0.818
##    48  22962    1412    0.770 0.00215        0.766        0.775
##    60  18526    1058    0.732 0.00234        0.727        0.737
##   120   4346    2477    0.576 0.00353        0.569        0.583
## 
##                 U_R_F=Rural 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5179     439    0.926 0.00341        0.919        0.932
##    24   4360     378    0.855 0.00470        0.846        0.865
##    36   3585     277    0.798 0.00551        0.787        0.809
##    48   2881     216    0.746 0.00619        0.734        0.758
##    60   2314     141    0.706 0.00670        0.693        0.719
##   120    516     332    0.549 0.00986        0.530        0.568
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  U_R_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ U_R_F, data = data)
## 
##   n= 351873, number of events= 87345 
##    (11037 observations deleted due to missingness)
## 
##                coef exp(coef) se(coef)     z Pr(>|z|)    
## U_R_FUrban 0.128419  1.137029 0.009531 13.47   <2e-16 ***
## U_R_FRural 0.227046  1.254888 0.023701  9.58   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##            exp(coef) exp(-coef) lower .95 upper .95
## U_R_FUrban     1.137     0.8795     1.116     1.158
## U_R_FRural     1.255     0.7969     1.198     1.315
## 
## Concordance= 0.509  (se = 0.001 )
## Rsquare= 0.001   (max possible= 0.998 )
## Likelihood ratio test= 249  on 2 df,   p=0
## Wald test            = 258.5  on 2 df,   p=0
## Score (logrank) test = 259.1  on 2 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  U_R_F

Class (treatment at performing facility)

uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)

## _________________________________________________
##    
## ## CLASS_OF_CASE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CLASS_OF_CASE_F, data = data)
## 
##                                     n events median 0.95LCL 0.95UCL
## CLASS_OF_CASE_F=Other_Facility   3475   1686   23.9    21.4    26.5
## CLASS_OF_CASE_F=All_Part_Prim  359435  88286  164.5   162.0      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CLASS_OF_CASE_F, data = data)
## 
##                 CLASS_OF_CASE_F=Other_Facility 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1572    1057    0.634 0.00910        0.616        0.652
##    24   1031     311    0.499 0.00989        0.480        0.518
##    36    720     143    0.422 0.01025        0.402        0.443
##    48    513      63    0.380 0.01052        0.360        0.401
##    60    390      35    0.351 0.01078        0.331        0.373
##   120     61      73    0.245 0.01402        0.219        0.274
## 
##                 CLASS_OF_CASE_F=All_Part_Prim 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 302585   20890    0.939 0.000411        0.938        0.940
##    24 260028   17312    0.883 0.000565        0.882        0.884
##    36 215359   13508    0.834 0.000673        0.833        0.835
##    48 177621    9886    0.793 0.000756        0.791        0.794
##    60 143912    7375    0.757 0.000829        0.756        0.759
##   120  34511   17116    0.616 0.001253        0.613        0.618
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  CLASS_OF_CASE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CLASS_OF_CASE_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                  coef exp(coef) se(coef)      z Pr(>|z|)
## CLASS_OF_CASE_FAll_Part_Prim -1.53139   0.21624  0.02463 -62.18   <2e-16
##                                 
## CLASS_OF_CASE_FAll_Part_Prim ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                              exp(coef) exp(-coef) lower .95 upper .95
## CLASS_OF_CASE_FAll_Part_Prim    0.2162      4.625     0.206    0.2269
## 
## Concordance= 0.51  (se = 0 )
## Rsquare= 0.007   (max possible= 0.998 )
## Likelihood ratio test= 2498  on 1 df,   p=0
## Wald test            = 3867  on 1 df,   p=0
## Score (logrank) test = 4684  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  CLASS_OF_CASE_F

Year

uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)

## _________________________________________________
##    
## ## YEAR_OF_DIAGNOSIS
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ YEAR_OF_DIAGNOSIS, data = data)
## 
##                            n events median 0.95LCL 0.95UCL
## YEAR_OF_DIAGNOSIS=2004 23341   8432     NA   164.6      NA
## YEAR_OF_DIAGNOSIS=2005 25400   8688  157.6   154.8      NA
## YEAR_OF_DIAGNOSIS=2006 25979   8817  143.3   143.3      NA
## YEAR_OF_DIAGNOSIS=2007 27201   8757     NA   131.0      NA
## YEAR_OF_DIAGNOSIS=2008 28353   8878     NA      NA      NA
## YEAR_OF_DIAGNOSIS=2009 29626   8524     NA   107.1      NA
## YEAR_OF_DIAGNOSIS=2010 29968   8076   95.5    95.0      NA
## YEAR_OF_DIAGNOSIS=2011 31345   7787     NA    83.3      NA
## YEAR_OF_DIAGNOSIS=2012 32394   6814   71.8    71.7      NA
## YEAR_OF_DIAGNOSIS=2013 34625   6286     NA      NA      NA
## YEAR_OF_DIAGNOSIS=2014 36169   5073     NA      NA      NA
## YEAR_OF_DIAGNOSIS=2015 38509   3840     NA      NA      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ YEAR_OF_DIAGNOSIS, data = data)
## 
##                 YEAR_OF_DIAGNOSIS=2004 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  20534    1362    0.940 0.00159        0.937        0.943
##    24  18897    1200    0.884 0.00216        0.880        0.888
##    36  17618    1006    0.837 0.00251        0.832        0.841
##    48  16580     775    0.799 0.00273        0.794        0.805
##    60  15531     700    0.765 0.00290        0.760        0.771
##   120   9840    2468    0.631 0.00344        0.624        0.638
## 
##                 YEAR_OF_DIAGNOSIS=2005 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  22550    1409    0.943 0.00148        0.940        0.946
##    24  20906    1253    0.890 0.00201        0.886        0.894
##    36  19614    1045    0.845 0.00234        0.841        0.850
##    48  18526     824    0.809 0.00255        0.804        0.814
##    60  17333     754    0.776 0.00272        0.771        0.781
##   120  10971    2625    0.646 0.00326        0.639        0.652
## 
##                 YEAR_OF_DIAGNOSIS=2006 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  22924    1546    0.939 0.00151        0.936        0.942
##    24  21237    1277    0.886 0.00203        0.882        0.890
##    36  19737    1178    0.836 0.00237        0.832        0.841
##    48  18347     997    0.794 0.00261        0.788        0.799
##    60  17024     804    0.758 0.00277        0.753        0.764
##   120   9515    2621    0.626 0.00330        0.619        0.632
## 
##                 YEAR_OF_DIAGNOSIS=2007 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  23826    1705    0.935 0.00152        0.932        0.938
##    24  21930    1425    0.879 0.00204        0.875        0.883
##    36  20397    1196    0.830 0.00236        0.826        0.835
##    48  19019     950    0.791 0.00256        0.786        0.796
##    60  17522     746    0.760 0.00271        0.754        0.765
##   120   4243    2625    0.621 0.00341        0.614        0.627
## 
##                 YEAR_OF_DIAGNOSIS=2008 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  24610    1800    0.934 0.00150        0.931        0.937
##    24  22521    1550    0.875 0.00203        0.871        0.879
##    36  20830    1248    0.826 0.00234        0.821        0.830
##    48  19203    1006    0.785 0.00255        0.780        0.790
##    60  17682     802    0.752 0.00270        0.747        0.757
##   120      3    2472    0.544 0.01943        0.507        0.583
## 
##                 YEAR_OF_DIAGNOSIS=2009 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  25522    1877    0.934 0.00147        0.931        0.937
##    24  23287    1598    0.875 0.00199        0.871        0.879
##    36  21389    1246    0.827 0.00229        0.823        0.832
##    48  19775    1009    0.788 0.00250        0.783        0.793
##    60  18056     860    0.753 0.00266        0.748        0.758
## 
##                 YEAR_OF_DIAGNOSIS=2010 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  25762    1960    0.932 0.00148        0.929        0.935
##    24  23583    1498    0.877 0.00196        0.873        0.881
##    36  21704    1283    0.829 0.00227        0.824        0.833
##    48  19856    1007    0.789 0.00248        0.785        0.794
##    60  17634     931    0.751 0.00266        0.746        0.756
## 
##                 YEAR_OF_DIAGNOSIS=2011 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  26669    1936    0.935 0.00142        0.932        0.938
##    24  24139    1711    0.874 0.00195        0.870        0.878
##    36  21935    1353    0.824 0.00226        0.820        0.829
##    48  19519    1101    0.782 0.00248        0.777        0.787
##    60  15871     866    0.745 0.00267        0.739        0.750
## 
##                 YEAR_OF_DIAGNOSIS=2012 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  27096    2010    0.934 0.00141        0.932        0.937
##    24  24362    1591    0.878 0.00191        0.874        0.882
##    36  21745    1319    0.829 0.00223        0.825        0.834
##    48  18039    1008    0.789 0.00246        0.784        0.794
##    60   7642     659    0.751 0.00277        0.746        0.757
## 
##                 YEAR_OF_DIAGNOSIS=2013 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  28214    2136    0.934 0.00138        0.932        0.937
##    24  24913    1639    0.878 0.00187        0.874        0.882
##    36  20818    1294    0.830 0.00219        0.826        0.834
##    48   9263     929    0.782 0.00259        0.777        0.788
##    60      7     288    0.630 0.03382        0.567        0.700
## 
##                 YEAR_OF_DIAGNOSIS=2014 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  28519    2126    0.936 0.00134        0.934        0.939
##    24  23349    1530    0.883 0.00183        0.879        0.886
##    36  10284    1074    0.831 0.00234        0.827        0.836
##    48      7     343    0.653 0.04472        0.571        0.746
## 
##                 YEAR_OF_DIAGNOSIS=2015 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  27931    2080    0.939 0.00129        0.937        0.942
##    24  11935    1351    0.880 0.00201        0.876        0.884
##    36      8     409    0.693 0.03072        0.635        0.756
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  YEAR_OF_DIAGNOSIS
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ YEAR_OF_DIAGNOSIS, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                           coef exp(coef) se(coef)      z Pr(>|z|)    
## YEAR_OF_DIAGNOSIS2005 -0.02829   0.97210  0.01540 -1.838 0.066132 .  
## YEAR_OF_DIAGNOSIS2006  0.04329   1.04424  0.01545  2.802 0.005084 ** 
## YEAR_OF_DIAGNOSIS2007  0.05790   1.05961  0.01555  3.722 0.000197 ***
## YEAR_OF_DIAGNOSIS2008  0.11329   1.11995  0.01557  7.276 3.45e-13 ***
## YEAR_OF_DIAGNOSIS2009  0.10857   1.11469  0.01578  6.880 5.97e-12 ***
## YEAR_OF_DIAGNOSIS2010  0.12528   1.13347  0.01605  7.807 5.88e-15 ***
## YEAR_OF_DIAGNOSIS2011  0.15966   1.17311  0.01625  9.823  < 2e-16 ***
## YEAR_OF_DIAGNOSIS2012  0.12014   1.12765  0.01688  7.119 1.09e-12 ***
## YEAR_OF_DIAGNOSIS2013  0.14330   1.15408  0.01732  8.275  < 2e-16 ***
## YEAR_OF_DIAGNOSIS2014  0.12010   1.12760  0.01849  6.494 8.38e-11 ***
## YEAR_OF_DIAGNOSIS2015  0.12723   1.13568  0.02030  6.267 3.68e-10 ***
## YEAR_OF_DIAGNOSIS2016       NA        NA  0.00000     NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                       exp(coef) exp(-coef) lower .95 upper .95
## YEAR_OF_DIAGNOSIS2005    0.9721     1.0287    0.9432     1.002
## YEAR_OF_DIAGNOSIS2006    1.0442     0.9576    1.0131     1.076
## YEAR_OF_DIAGNOSIS2007    1.0596     0.9437    1.0278     1.092
## YEAR_OF_DIAGNOSIS2008    1.1200     0.8929    1.0863     1.155
## YEAR_OF_DIAGNOSIS2009    1.1147     0.8971    1.0807     1.150
## YEAR_OF_DIAGNOSIS2010    1.1335     0.8822    1.0984     1.170
## YEAR_OF_DIAGNOSIS2011    1.1731     0.8524    1.1363     1.211
## YEAR_OF_DIAGNOSIS2012    1.1277     0.8868    1.0910     1.166
## YEAR_OF_DIAGNOSIS2013    1.1541     0.8665    1.1156     1.194
## YEAR_OF_DIAGNOSIS2014    1.1276     0.8868    1.0875     1.169
## YEAR_OF_DIAGNOSIS2015    1.1357     0.8805    1.0914     1.182
## YEAR_OF_DIAGNOSIS2016        NA         NA        NA        NA
## 
## Concordance= 0.511  (se = 0.001 )
## Rsquare= 0.001   (max possible= 0.998 )
## Likelihood ratio test= 264  on 11 df,   p=0
## Wald test            = 260  on 11 df,   p=0
## Score (logrank) test = 260.3  on 11 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  YEAR_OF_DIAGNOSIS

Primary Site

uni_var(test_var = "SITE_TEXT", data_imp = data)

## _________________________________________________
##    
## ## SITE_TEXT
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SITE_TEXT, data = data)
## 
##                                                                n events
## SITE_TEXT=C44.0 Skin of lip, NOS                             631    187
## SITE_TEXT=C44.1 Eyelid                                      1184    380
## SITE_TEXT=C44.2 External ear                               10639   3149
## SITE_TEXT=C44.3 Skin of ear and unspecified parts of face  33680  10586
## SITE_TEXT=C44.4 Skin of scalp and neck                     30276   9995
## SITE_TEXT=C44.5 Skin of trunk                             113276  23471
## SITE_TEXT=C44.6 Skin of upper limb and shoulder            90064  19354
## SITE_TEXT=C44.7 Skin of lower limb and hip                 67737  12311
## SITE_TEXT=C44.8 Overlapping lesion of skin                   389    143
## SITE_TEXT=C44.9 Skin, NOS                                  15034  10396
##                                                           median 0.95LCL
## SITE_TEXT=C44.0 Skin of lip, NOS                             155   118.2
## SITE_TEXT=C44.1 Eyelid                                       122   109.5
## SITE_TEXT=C44.2 External ear                                 131   125.6
## SITE_TEXT=C44.3 Skin of ear and unspecified parts of face    118   115.7
## SITE_TEXT=C44.4 Skin of scalp and neck                       114   111.1
## SITE_TEXT=C44.5 Skin of trunk                                 NA      NA
## SITE_TEXT=C44.6 Skin of upper limb and shoulder              164   162.7
## SITE_TEXT=C44.7 Skin of lower limb and hip                    NA      NA
## SITE_TEXT=C44.8 Overlapping lesion of skin                   113    87.0
## SITE_TEXT=C44.9 Skin, NOS                                     11    10.6
##                                                           0.95UCL
## SITE_TEXT=C44.0 Skin of lip, NOS                               NA
## SITE_TEXT=C44.1 Eyelid                                      139.3
## SITE_TEXT=C44.2 External ear                                138.2
## SITE_TEXT=C44.3 Skin of ear and unspecified parts of face   121.2
## SITE_TEXT=C44.4 Skin of scalp and neck                      116.7
## SITE_TEXT=C44.5 Skin of trunk                                  NA
## SITE_TEXT=C44.6 Skin of upper limb and shoulder                NA
## SITE_TEXT=C44.7 Skin of lower limb and hip                     NA
## SITE_TEXT=C44.8 Overlapping lesion of skin                     NA
## SITE_TEXT=C44.9 Skin, NOS                                    11.5
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SITE_TEXT, data = data)
## 
##                 SITE_TEXT=C44.0 Skin of lip, NOS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    535      36    0.940 0.00971        0.921        0.959
##    24    467      37    0.873 0.01394        0.846        0.901
##    36    391      31    0.812 0.01673        0.780        0.845
##    48    312      21    0.764 0.01871        0.728        0.802
##    60    253      21    0.710 0.02085        0.670        0.752
##   120     53      38    0.541 0.03086        0.484        0.605
## 
##                 SITE_TEXT=C44.1 Eyelid 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1026      60    0.947 0.00669        0.934        0.960
##    24    862      78    0.872 0.01021        0.852        0.892
##    36    714      55    0.813 0.01224        0.789        0.837
##    48    582      49    0.754 0.01397        0.727        0.782
##    60    473      42    0.696 0.01551        0.666        0.727
##   120    105      86    0.503 0.02240        0.461        0.549
## 
##                 SITE_TEXT=C44.2 External ear 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9051     556    0.945 0.00229        0.940        0.949
##    24   7649     612    0.878 0.00336        0.871        0.884
##    36   6310     499    0.817 0.00409        0.809        0.825
##    48   5107     412    0.760 0.00467        0.751        0.769
##    60   4109     269    0.716 0.00510        0.707        0.727
##   120    905     711    0.526 0.00766        0.511        0.541
## 
##                 SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  28750    1761    0.945 0.00128        0.942        0.947
##    24  24359    2104    0.873 0.00192        0.869        0.876
##    36  19836    1673    0.809 0.00233        0.804        0.813
##    48  16060    1335    0.750 0.00266        0.745        0.756
##    60  12693    1078    0.696 0.00294        0.690        0.702
##   120   2658    2351    0.494 0.00437        0.486        0.503
## 
##                 SITE_TEXT=C44.4 Skin of scalp and neck 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  25477    2029    0.929 0.00152        0.926        0.932
##    24  21131    2204    0.845 0.00219        0.841        0.850
##    36  16769    1763    0.770 0.00263        0.765        0.775
##    48  13283    1211    0.710 0.00294        0.704        0.716
##    60  10447     850    0.661 0.00319        0.654        0.667
##   120   2165    1788    0.483 0.00459        0.474        0.492
## 
##                 SITE_TEXT=C44.5 Skin of trunk 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  96604    4575    0.957 0.000622        0.956        0.958
##    24  83748    4741    0.908 0.000911        0.906        0.910
##    36  69838    3766    0.865 0.001110        0.862        0.867
##    48  58021    2849    0.827 0.001267        0.824        0.829
##    60  47338    2011    0.796 0.001397        0.793        0.798
##   120  11762    4872    0.667 0.002168        0.662        0.671
## 
##                 SITE_TEXT=C44.6 Skin of upper limb and shoulder 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  77412    3180    0.962 0.000654        0.961        0.964
##    24  67145    3771    0.914 0.000994        0.912        0.916
##    36  55975    3084    0.869 0.001230        0.866        0.871
##    48  46240    2311    0.830 0.001414        0.827        0.833
##    60  37565    1791    0.795 0.001577        0.792        0.798
##   120   8927    4580    0.641 0.002558        0.636        0.646
## 
##                 SITE_TEXT=C44.7 Skin of lower limb and hip 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  58436    2223    0.965 0.000727        0.964        0.967
##    24  50972    2547    0.921 0.001096        0.919        0.923
##    36  42817    2116    0.881 0.001359        0.878        0.883
##    48  35910    1459    0.849 0.001549        0.845        0.852
##    60  29399    1163    0.819 0.001724        0.815        0.822
##   120   7556    2467    0.711 0.002634        0.706        0.717
## 
##                 SITE_TEXT=C44.8 Overlapping lesion of skin 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    307      52    0.860  0.0180        0.826        0.896
##    24    262      23    0.794  0.0213        0.754        0.837
##    36    218      21    0.727  0.0240        0.682        0.776
##    48    172      11    0.688  0.0254        0.640        0.740
##    60    144       8    0.653  0.0269        0.603        0.708
##   120     29      26    0.473  0.0388        0.402        0.555
## 
##                 SITE_TEXT=C44.9 Skin, NOS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6559    7475    0.484 0.00418        0.476        0.492
##    24   4464    1506    0.368 0.00411        0.360        0.376
##    36   3211     643    0.311 0.00405        0.303        0.319
##    48   2447     291    0.280 0.00403        0.273        0.288
##    60   1881     177    0.258 0.00404        0.251        0.266
##   120    412     270    0.201 0.00460        0.192        0.210
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  SITE_TEXT
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SITE_TEXT, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                                              coef
## SITE_TEXTC44.1 Eyelid                                     0.08145
## SITE_TEXTC44.2 External ear                               0.02387
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face  0.09618
## SITE_TEXTC44.4 Skin of scalp and neck                     0.19201
## SITE_TEXTC44.5 Skin of trunk                             -0.38193
## SITE_TEXTC44.6 Skin of upper limb and shoulder           -0.34458
## SITE_TEXTC44.7 Skin of lower limb and hip                -0.53944
## SITE_TEXTC44.8 Overlapping lesion of skin                 0.30726
## SITE_TEXTC44.9 Skin, NOS                                  1.67933
##                                                          exp(coef)
## SITE_TEXTC44.1 Eyelid                                      1.08485
## SITE_TEXTC44.2 External ear                                1.02416
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face   1.10095
## SITE_TEXTC44.4 Skin of scalp and neck                      1.21168
## SITE_TEXTC44.5 Skin of trunk                               0.68254
## SITE_TEXTC44.6 Skin of upper limb and shoulder             0.70852
## SITE_TEXTC44.7 Skin of lower limb and hip                  0.58308
## SITE_TEXTC44.8 Overlapping lesion of skin                  1.35970
## SITE_TEXTC44.9 Skin, NOS                                   5.36195
##                                                          se(coef)      z
## SITE_TEXTC44.1 Eyelid                                     0.08933  0.912
## SITE_TEXTC44.2 External ear                               0.07527  0.317
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face  0.07377  1.304
## SITE_TEXTC44.4 Skin of scalp and neck                     0.07381  2.601
## SITE_TEXTC44.5 Skin of trunk                              0.07342 -5.202
## SITE_TEXTC44.6 Skin of upper limb and shoulder            0.07348 -4.689
## SITE_TEXTC44.7 Skin of lower limb and hip                 0.07368 -7.321
## SITE_TEXTC44.8 Overlapping lesion of skin                 0.11109  2.766
## SITE_TEXTC44.9 Skin, NOS                                  0.07379 22.758
##                                                          Pr(>|z|)    
## SITE_TEXTC44.1 Eyelid                                     0.36189    
## SITE_TEXTC44.2 External ear                               0.75110    
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face  0.19233    
## SITE_TEXTC44.4 Skin of scalp and neck                     0.00928 ** 
## SITE_TEXTC44.5 Skin of trunk                             1.97e-07 ***
## SITE_TEXTC44.6 Skin of upper limb and shoulder           2.74e-06 ***
## SITE_TEXTC44.7 Skin of lower limb and hip                2.46e-13 ***
## SITE_TEXTC44.8 Overlapping lesion of skin                 0.00568 ** 
## SITE_TEXTC44.9 Skin, NOS                                  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                                          exp(coef)
## SITE_TEXTC44.1 Eyelid                                       1.0849
## SITE_TEXTC44.2 External ear                                 1.0242
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face    1.1010
## SITE_TEXTC44.4 Skin of scalp and neck                       1.2117
## SITE_TEXTC44.5 Skin of trunk                                0.6825
## SITE_TEXTC44.6 Skin of upper limb and shoulder              0.7085
## SITE_TEXTC44.7 Skin of lower limb and hip                   0.5831
## SITE_TEXTC44.8 Overlapping lesion of skin                   1.3597
## SITE_TEXTC44.9 Skin, NOS                                    5.3620
##                                                          exp(-coef)
## SITE_TEXTC44.1 Eyelid                                        0.9218
## SITE_TEXTC44.2 External ear                                  0.9764
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face     0.9083
## SITE_TEXTC44.4 Skin of scalp and neck                        0.8253
## SITE_TEXTC44.5 Skin of trunk                                 1.4651
## SITE_TEXTC44.6 Skin of upper limb and shoulder               1.4114
## SITE_TEXTC44.7 Skin of lower limb and hip                    1.7150
## SITE_TEXTC44.8 Overlapping lesion of skin                    0.7355
## SITE_TEXTC44.9 Skin, NOS                                     0.1865
##                                                          lower .95
## SITE_TEXTC44.1 Eyelid                                       0.9106
## SITE_TEXTC44.2 External ear                                 0.8837
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face    0.9527
## SITE_TEXTC44.4 Skin of scalp and neck                       1.0485
## SITE_TEXTC44.5 Skin of trunk                                0.5911
## SITE_TEXTC44.6 Skin of upper limb and shoulder              0.6135
## SITE_TEXTC44.7 Skin of lower limb and hip                   0.5047
## SITE_TEXTC44.8 Overlapping lesion of skin                   1.0937
## SITE_TEXTC44.9 Skin, NOS                                    4.6399
##                                                          upper .95
## SITE_TEXTC44.1 Eyelid                                       1.2924
## SITE_TEXTC44.2 External ear                                 1.1870
## SITE_TEXTC44.3 Skin of ear and unspecified parts of face    1.2722
## SITE_TEXTC44.4 Skin of scalp and neck                       1.4003
## SITE_TEXTC44.5 Skin of trunk                                0.7882
## SITE_TEXTC44.6 Skin of upper limb and shoulder              0.8183
## SITE_TEXTC44.7 Skin of lower limb and hip                   0.6737
## SITE_TEXTC44.8 Overlapping lesion of skin                   1.6904
## SITE_TEXTC44.9 Skin, NOS                                    6.1963
## 
## Concordance= 0.617  (se = 0.001 )
## Rsquare= 0.07   (max possible= 0.998 )
## Likelihood ratio test= 26455  on 9 df,   p=0
## Wald test            = 38543  on 9 df,   p=0
## Score (logrank) test = 51507  on 9 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  SITE_TEXT

Histology

uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)

## _________________________________________________
##    
## ## HISTOLOGY_F_LIM
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISTOLOGY_F_LIM, data = data)
## 
##                            n events median 0.95LCL 0.95UCL
## HISTOLOGY_F_LIM=8720  182616  49264  162.7   160.3   165.2
## HISTOLOGY_F_LIM=8743  112470  17259     NA      NA      NA
## HISTOLOGY_F_LIM=8742   19251   4953  129.7   125.6   133.9
## HISTOLOGY_F_LIM=Other  48573  18496   95.9    93.1    97.9
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISTOLOGY_F_LIM, data = data)
## 
##                 HISTOLOGY_F_LIM=8720 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 149938   14977    0.914 0.000673        0.913        0.915
##    24 129130    9114    0.856 0.000861        0.855        0.858
##    36 108073    6738    0.809 0.000988        0.807        0.811
##    48  89985    4912    0.770 0.001088        0.767        0.772
##    60  73642    3670    0.736 0.001175        0.733        0.738
##   120  18210    8710    0.600 0.001712        0.597        0.603
## 
##                 HISTOLOGY_F_LIM=8743 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  97178    2303    0.978 0.000454        0.977        0.979
##    24  84928    3105    0.945 0.000726        0.944        0.947
##    36  70760    2841    0.911 0.000939        0.909        0.913
##    48  58608    2255    0.880 0.001115        0.878        0.882
##    60  47353    1812    0.850 0.001278        0.848        0.853
##   120  11407    4355    0.724 0.002196        0.720        0.728
## 
##                 HISTOLOGY_F_LIM=8742 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  16438     630    0.965 0.00137        0.962        0.968
##    24  14192     813    0.915 0.00215        0.911        0.919
##    36  11594     790    0.860 0.00276        0.855        0.866
##    48   9298     676    0.806 0.00329        0.800        0.812
##    60   7377     506    0.758 0.00372        0.751        0.766
##   120   1477    1368    0.530 0.00623        0.518        0.543
## 
##                 HISTOLOGY_F_LIM=Other 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  40603    4037    0.913 0.00131        0.910        0.915
##    24  32809    4591    0.806 0.00189        0.802        0.809
##    36  25652    3282    0.720 0.00220        0.715        0.724
##    48  20243    2106    0.656 0.00240        0.652        0.661
##    60  15930    1422    0.607 0.00256        0.602        0.612
##   120   3478    2756    0.447 0.00341        0.441        0.454
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  HISTOLOGY_F_LIM
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ HISTOLOGY_F_LIM, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                           coef exp(coef)  se(coef)       z Pr(>|z|)    
## HISTOLOGY_F_LIM9680         NA        NA  0.000000      NA       NA    
## HISTOLOGY_F_LIM8743  -0.609418  0.543667  0.008846 -68.895   <2e-16 ***
## HISTOLOGY_F_LIM8742  -0.033942  0.966628  0.014909  -2.277   0.0228 *  
## HISTOLOGY_F_LIMOther  0.439458  1.551866  0.008632  50.911   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                      exp(coef) exp(-coef) lower .95 upper .95
## HISTOLOGY_F_LIM9680         NA         NA        NA        NA
## HISTOLOGY_F_LIM8743     0.5437     1.8394    0.5343    0.5532
## HISTOLOGY_F_LIM8742     0.9666     1.0345    0.9388    0.9953
## HISTOLOGY_F_LIMOther    1.5519     0.6444    1.5258    1.5783
## 
## Concordance= 0.599  (se = 0.001 )
## Rsquare= 0.028   (max possible= 0.998 )
## Likelihood ratio test= 10352  on 3 df,   p=0
## Wald test            = 9905  on 3 df,   p=0
## Score (logrank) test = 10430  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  HISTOLOGY_F_LIM

Grade

uni_var(test_var = "GRADE_F", data_imp = data)

## _________________________________________________
##    
## ## GRADE_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ GRADE_F, data = data)
## 
##                                       n events median 0.95LCL 0.95UCL
## GRADE_F=Gr I: Well Diff             759    178     NA   140.5      NA
## GRADE_F=Gr II: Mod Diff            1064    243     NA   156.6      NA
## GRADE_F=Gr III: Poor Diff          1877    975   46.8    40.9    55.1
## GRADE_F=Gr IV: Undiff/Anaplastic    665    287   73.0    59.9   101.7
## GRADE_F=NA/Unkown                358545  88289  164.5   161.9      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ GRADE_F, data = data)
## 
##                 GRADE_F=Gr I: Well Diff 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    641      33    0.953 0.00793        0.938        0.969
##    24    540      39    0.892 0.01203        0.869        0.916
##    36    437      28    0.842 0.01464        0.814        0.871
##    48    346      14    0.812 0.01620        0.781        0.844
##    60    292      15    0.774 0.01816        0.739        0.810
##   120     73      45    0.602 0.02816        0.549        0.660
## 
##                 GRADE_F=Gr II: Mod Diff 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    925      41    0.959 0.00630        0.947        0.971
##    24    814      53    0.902 0.00962        0.883        0.921
##    36    695      36    0.860 0.01148        0.837        0.882
##    48    600      22    0.831 0.01259        0.807        0.856
##    60    506      22    0.799 0.01386        0.772        0.827
##   120    140      64    0.654 0.02061        0.615        0.696
## 
##                 GRADE_F=Gr III: Poor Diff 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1287     456    0.748  0.0102        0.728        0.768
##    24    968     211    0.620  0.0117        0.598        0.644
##    36    770     104    0.550  0.0122        0.527        0.574
##    48    615      73    0.495  0.0126        0.471        0.520
##    60    495      34    0.466  0.0128        0.441        0.491
##   120    114      85    0.351  0.0151        0.322        0.382
## 
##                 GRADE_F=Gr IV: Undiff/Anaplastic 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    492     111    0.824  0.0152        0.795        0.854
##    24    385      62    0.715  0.0185        0.680        0.752
##    36    297      42    0.630  0.0204        0.592        0.672
##    48    243      23    0.579  0.0214        0.539        0.623
##    60    198      15    0.541  0.0221        0.500        0.586
##   120     44      32    0.401  0.0289        0.348        0.462
## 
##                 GRADE_F=NA/Unkown 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 300812   21306    0.937 0.000416        0.937        0.938
##    24 258352   17258    0.881 0.000570        0.880        0.882
##    36 213880   13441    0.832 0.000677        0.831        0.834
##    48 176330    9817    0.791 0.000760        0.790        0.793
##    60 142811    7324    0.756 0.000832        0.754        0.757
##   120  34201   16963    0.614 0.001257        0.612        0.617
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  GRADE_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ GRADE_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                     coef exp(coef) se(coef)      z
## GRADE_FGr II: Mod Diff          -0.15501   0.85640  0.09866 -1.571
## GRADE_FGr III: Poor Diff         1.08725   2.96610  0.08151 13.339
## GRADE_FGr IV: Undiff/Anaplastic  0.81123   2.25069  0.09541  8.503
## GRADE_F5                              NA        NA  0.00000     NA
## GRADE_F6                              NA        NA  0.00000     NA
## GRADE_F7                              NA        NA  0.00000     NA
## GRADE_F8                              NA        NA  0.00000     NA
## GRADE_FNA/Unkown                 0.03965   1.04044  0.07503  0.528
##                                 Pr(>|z|)    
## GRADE_FGr II: Mod Diff             0.116    
## GRADE_FGr III: Poor Diff          <2e-16 ***
## GRADE_FGr IV: Undiff/Anaplastic   <2e-16 ***
## GRADE_F5                              NA    
## GRADE_F6                              NA    
## GRADE_F7                              NA    
## GRADE_F8                              NA    
## GRADE_FNA/Unkown                   0.597    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                 exp(coef) exp(-coef) lower .95 upper .95
## GRADE_FGr II: Mod Diff             0.8564     1.1677    0.7058     1.039
## GRADE_FGr III: Poor Diff           2.9661     0.3371    2.5282     3.480
## GRADE_FGr IV: Undiff/Anaplastic    2.2507     0.4443    1.8668     2.713
## GRADE_F5                               NA         NA        NA        NA
## GRADE_F6                               NA         NA        NA        NA
## GRADE_F7                               NA         NA        NA        NA
## GRADE_F8                               NA         NA        NA        NA
## GRADE_FNA/Unkown                   1.0404     0.9611    0.8982     1.205
## 
## Concordance= 0.506  (se = 0 )
## Rsquare= 0.003   (max possible= 0.998 )
## Likelihood ratio test= 915.1  on 4 df,   p=0
## Wald test            = 1234  on 4 df,   p=0
## Score (logrank) test = 1343  on 4 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  GRADE_F

Clinical T Stage

uni_var(test_var = "TNM_CLIN_T", data_imp = data)

## _________________________________________________
##    
## ## TNM_CLIN_T
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_T, data = data)
## 
##    9794 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## TNM_CLIN_T=N_A   1608    548  160.3   150.7      NA
## TNM_CLIN_T=c0    4261   2744   14.1    12.7    15.3
## TNM_CLIN_T=c1   18911   2882     NA      NA      NA
## TNM_CLIN_T=c1A  55175   4305   95.5    95.5      NA
## TNM_CLIN_T=c1B  20634   2001   97.1      NA      NA
## TNM_CLIN_T=c2    5403   1306  153.7   141.8      NA
## TNM_CLIN_T=c2A  34068   5945  164.4   162.0      NA
## TNM_CLIN_T=c2B   8717   2535  110.8   104.6   122.2
## TNM_CLIN_T=c3    3178   1210   89.6    81.8    96.4
## TNM_CLIN_T=c3A  13377   3947  113.4   109.1   118.6
## TNM_CLIN_T=c3B  10395   4261   69.7    66.3    72.0
## TNM_CLIN_T=c4    2170   1190   41.9    37.9    45.3
## TNM_CLIN_T=c4A   6281   2644   70.0    65.6    74.2
## TNM_CLIN_T=c4B  10655   6264   34.2    32.9    35.4
## TNM_CLIN_T=cX  155744  45656  165.4   164.6      NA
## TNM_CLIN_T=pIS   2539    465     NA   157.9      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_T, data = data)
## 
## 9794 observations deleted due to missingness 
##                 TNM_CLIN_T=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1405     113    0.928 0.00655        0.915        0.941
##    24   1274      98    0.862 0.00883        0.845        0.880
##    36   1170      67    0.816 0.00998        0.797        0.836
##    48   1073      61    0.773 0.01089        0.752        0.794
##    60    986      40    0.743 0.01142        0.721        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_CLIN_T=c0 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2014    1958    0.524 0.00783        0.509        0.540
##    24   1383     415    0.410 0.00788        0.395        0.426
##    36    980     177    0.353 0.00788        0.338        0.369
##    48    726      84    0.320 0.00793        0.305        0.336
##    60    530      40    0.300 0.00804        0.285        0.316
##   120     87      59    0.242 0.00993        0.224        0.263
## 
##                 TNM_CLIN_T=c1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  16419     403    0.977 0.00112        0.975        0.979
##    24  14824     460    0.949 0.00170        0.946        0.952
##    36  12980     464    0.918 0.00218        0.913        0.922
##    48  11002     352    0.891 0.00253        0.886        0.896
##    60   8896     316    0.863 0.00290        0.857        0.869
##   120   1502     801    0.720 0.00579        0.709        0.732
## 
##                 TNM_CLIN_T=c1A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  45816     779    0.984 0.000555        0.983        0.986
##    24  38151     945    0.963 0.000888        0.961        0.964
##    36  28525     852    0.938 0.001198        0.936        0.941
##    48  19945     721    0.910 0.001552        0.907        0.913
##    60  12428     513    0.881 0.001964        0.878        0.885
## 
##                 TNM_CLIN_T=c1B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  17122     385    0.980 0.00103        0.978        0.982
##    24  13908     510    0.948 0.00170        0.945        0.951
##    36   9901     420    0.915 0.00229        0.910        0.919
##    48   6635     305    0.881 0.00291        0.876        0.887
##    60   3871     204    0.847 0.00367        0.840        0.854
## 
##                 TNM_CLIN_T=c2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4665     211    0.959 0.00280        0.953        0.964
##    24   4129     242    0.907 0.00417        0.899        0.915
##    36   3503     212    0.858 0.00513        0.848        0.868
##    48   2947     168    0.814 0.00587        0.803        0.826
##    60   2326     161    0.766 0.00665        0.753        0.779
##   120    303     287    0.592 0.01193        0.569        0.616
## 
##                 TNM_CLIN_T=c2A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  29464     759    0.976 0.000856        0.974        0.978
##    24  25227    1136    0.936 0.001419        0.934        0.939
##    36  20248    1037    0.894 0.001862        0.891        0.898
##    48  16002     862    0.853 0.002255        0.848        0.857
##    60  12260     680    0.812 0.002631        0.807        0.817
##   120   1826    1343    0.645 0.005145        0.635        0.656
## 
##                 TNM_CLIN_T=c2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7387     437    0.947 0.00249        0.942        0.951
##    24   6074     590    0.867 0.00387        0.860        0.875
##    36   4657     499    0.790 0.00485        0.780        0.799
##    48   3501     345    0.725 0.00556        0.714        0.736
##    60   2540     243    0.669 0.00619        0.657        0.681
##   120    314     391    0.483 0.01043        0.463        0.503
## 
##                 TNM_CLIN_T=c3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2678     250    0.917 0.00500        0.908        0.927
##    24   2266     256    0.827 0.00700        0.814        0.841
##    36   1815     232    0.738 0.00836        0.722        0.754
##    48   1412     162    0.667 0.00922        0.650        0.686
##    60   1070     114    0.609 0.00992        0.590        0.629
##   120    129     188    0.425 0.01512        0.397        0.456
## 
##                 TNM_CLIN_T=c3A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  11497     582    0.954 0.00187        0.950        0.957
##    24   9564     878    0.878 0.00301        0.872        0.884
##    36   7351     784    0.799 0.00383        0.792        0.807
##    48   5649     573    0.731 0.00444        0.723        0.740
##    60   4221     389    0.676 0.00492        0.666        0.685
##   120    589     689    0.479 0.00826        0.463        0.495
## 
##                 TNM_CLIN_T=c3B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   8723     818    0.917 0.00277        0.912        0.923
##    24   6761    1160    0.789 0.00423        0.781        0.798
##    36   4905     859    0.681 0.00503        0.671        0.690
##    48   3581     530    0.600 0.00552        0.590        0.611
##    60   2570     328    0.539 0.00591        0.528        0.551
##   120    295     527    0.344 0.00888        0.327        0.362
## 
##                 TNM_CLIN_T=c4 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1605     425    0.797 0.00882        0.780        0.814
##    24   1184     310    0.637 0.01075        0.616        0.659
##    36    889     178    0.537 0.01140        0.515        0.560
##    48    662     113    0.464 0.01175        0.441        0.487
##    60    501      67    0.413 0.01199        0.390        0.437
##   120     59      95    0.280 0.01600        0.250        0.313
## 
##                 TNM_CLIN_T=c4A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5224     580    0.904 0.00380        0.896        0.911
##    24   4043     712    0.775 0.00554        0.764        0.786
##    36   3049     475    0.677 0.00641        0.665        0.690
##    48   2280     334    0.597 0.00700        0.583        0.611
##    60   1651     210    0.536 0.00745        0.521        0.551
##   120    226     318    0.360 0.01061        0.339        0.381
## 
##                 TNM_CLIN_T=c4B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   8023    2025    0.804 0.00392        0.796        0.811
##    24   5505    1854    0.610 0.00493        0.600        0.620
##    36   3705    1051    0.485 0.00522        0.475        0.495
##    48   2521     581    0.401 0.00536        0.391        0.412
##    60   1742     299    0.349 0.00546        0.338        0.359
##   120    160     444    0.194 0.00736        0.180        0.209
## 
##                 TNM_CLIN_T=cX 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 132133   11393    0.924 0.000686        0.923        0.925
##    24 119023    7409    0.871 0.000881        0.869        0.873
##    36 106890    5972    0.826 0.001009        0.824        0.828
##    48  96220    4458    0.791 0.001097        0.788        0.793
##    60  86053    3647    0.760 0.001168        0.757        0.762
##   120  28530   11018    0.630 0.001528        0.627        0.633
## 
##                 TNM_CLIN_T=pIS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2192      63    0.974 0.00329        0.967        0.980
##    24   1850      88    0.932 0.00536        0.922        0.943
##    36   1493      77    0.890 0.00697        0.876        0.903
##    48   1164      66    0.847 0.00841        0.830        0.863
##    60    892      49    0.807 0.00975        0.788        0.826
##   120    152     115    0.623 0.01838        0.588        0.660
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_T
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_T, data = data)
## 
##   n= 353116, number of events= 87903 
##    (9794 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)       z Pr(>|z|)    
## TNM_CLIN_Tc0   1.70956   5.52651  0.04684  36.494  < 2e-16 ***
## TNM_CLIN_Tc1  -0.57711   0.56152  0.04662 -12.379  < 2e-16 ***
## TNM_CLIN_Tc1A -0.89914   0.40692  0.04543 -19.792  < 2e-16 ***
## TNM_CLIN_Tc1B -0.63446   0.53022  0.04829 -13.138  < 2e-16 ***
## TNM_CLIN_Tc2  -0.06001   0.94175  0.05092  -1.179    0.239    
## TNM_CLIN_Tc2A -0.31573   0.72926  0.04467  -7.068 1.57e-12 ***
## TNM_CLIN_Tc2B  0.29119   1.33802  0.04715   6.176 6.57e-10 ***
## TNM_CLIN_Tc2C       NA        NA  0.00000      NA       NA    
## TNM_CLIN_Tc3   0.51767   1.67811  0.05152  10.048  < 2e-16 ***
## TNM_CLIN_Tc3A  0.26915   1.30885  0.04562   5.900 3.64e-09 ***
## TNM_CLIN_Tc3B  0.71220   2.03847  0.04543  15.677  < 2e-16 ***
## TNM_CLIN_Tc4   1.10491   3.01896  0.05166  21.387  < 2e-16 ***
## TNM_CLIN_Tc4A  0.71881   2.05200  0.04698  15.302  < 2e-16 ***
## TNM_CLIN_Tc4B  1.28103   3.60035  0.04461  28.714  < 2e-16 ***
## TNM_CLIN_TcX  -0.05344   0.94796  0.04298  -1.243    0.214    
## TNM_CLIN_TpIS -0.26369   0.76821  0.06307  -4.181 2.90e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## TNM_CLIN_Tc0     5.5265     0.1809    5.0417    6.0579
## TNM_CLIN_Tc1     0.5615     1.7809    0.5125    0.6152
## TNM_CLIN_Tc1A    0.4069     2.4575    0.3723    0.4448
## TNM_CLIN_Tc1B    0.5302     1.8860    0.4823    0.5829
## TNM_CLIN_Tc2     0.9418     1.0619    0.8523    1.0406
## TNM_CLIN_Tc2A    0.7293     1.3713    0.6681    0.7960
## TNM_CLIN_Tc2B    1.3380     0.7474    1.2199    1.4676
## TNM_CLIN_Tc2C        NA         NA        NA        NA
## TNM_CLIN_Tc3     1.6781     0.5959    1.5169    1.8564
## TNM_CLIN_Tc3A    1.3089     0.7640    1.1969    1.4313
## TNM_CLIN_Tc3B    2.0385     0.4906    1.8648    2.2283
## TNM_CLIN_Tc4     3.0190     0.3312    2.7282    3.3407
## TNM_CLIN_Tc4A    2.0520     0.4873    1.8715    2.2499
## TNM_CLIN_Tc4B    3.6004     0.2778    3.2989    3.9293
## TNM_CLIN_TcX     0.9480     1.0549    0.8714    1.0313
## TNM_CLIN_TpIS    0.7682     1.3017    0.6789    0.8693
## 
## Concordance= 0.64  (se = 0.001 )
## Rsquare= 0.066   (max possible= 0.998 )
## Likelihood ratio test= 24210  on 15 df,   p=0
## Wald test            = 28661  on 15 df,   p=0
## Score (logrank) test = 34935  on 15 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_T

Clinical N Stage

uni_var(test_var = "TNM_CLIN_N", data_imp = data)

## _________________________________________________
##    
## ## TNM_CLIN_N
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_N, data = data)
## 
##    8298 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## TNM_CLIN_N=N_A   1608    548  160.3   150.7      NA
## TNM_CLIN_N=c0  247096  47331     NA   164.6      NA
## TNM_CLIN_N=c1    5666   2838   43.6    40.5    47.0
## TNM_CLIN_N=c1A   1325    516  110.8    93.4   131.8
## TNM_CLIN_N=c1B   1624    954   28.6    26.1    33.0
## TNM_CLIN_N=c2    1148    630   33.9    31.3    39.2
## TNM_CLIN_N=c2A    415    186   77.7    67.0   118.9
## TNM_CLIN_N=c2B    805    487   29.2    26.1    33.3
## TNM_CLIN_N=c2C    900    479   38.7    35.5    45.7
## TNM_CLIN_N=c3    2603   1825   15.7    14.8    17.1
## TNM_CLIN_N=cX   91422  32280  155.9   153.6   158.2
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_N, data = data)
## 
## 8298 observations deleted due to missingness 
##                 TNM_CLIN_N=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1405     113    0.928 0.00655        0.915        0.941
##    24   1274      98    0.862 0.00883        0.845        0.880
##    36   1170      67    0.816 0.00998        0.797        0.836
##    48   1073      61    0.773 0.01089        0.752        0.794
##    60    986      40    0.743 0.01142        0.721        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_CLIN_N=c0 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 210265    8609    0.963 0.000394        0.962        0.964
##    24 179841    9675    0.916 0.000595        0.915        0.917
##    36 145477    8175    0.871 0.000746        0.870        0.873
##    48 116031    6081    0.831 0.000870        0.830        0.833
##    60  89945    4538    0.795 0.000983        0.794        0.797
##   120  14585    9384    0.646 0.001775        0.643        0.650
## 
##                 TNM_CLIN_N=c1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3995    1271    0.767 0.00574        0.756        0.778
##    24   2926     691    0.628 0.00671        0.615        0.641
##    36   2138     385    0.539 0.00714        0.525        0.553
##    48   1585     206    0.483 0.00740        0.468        0.497
##    60   1145     117    0.443 0.00766        0.428        0.458
##   120    137     155    0.332 0.01086        0.311        0.354
## 
##                 TNM_CLIN_N=c1A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1160      95    0.926 0.00731        0.912        0.940
##    24    962     129    0.820 0.01092        0.799        0.842
##    36    797      96    0.735 0.01278        0.710        0.760
##    48    649      73    0.665 0.01396        0.638        0.693
##    60    524      43    0.617 0.01475        0.589        0.646
##   120     94      74    0.471 0.02050        0.432        0.513
## 
##                 TNM_CLIN_N=c1B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1085     442    0.719  0.0114        0.697        0.741
##    24    769     247    0.550  0.0128        0.525        0.575
##    36    553     115    0.462  0.0131        0.437        0.489
##    48    419      71    0.399  0.0133        0.373        0.426
##    60    330      22    0.376  0.0134        0.350        0.403
##   120     50      52    0.278  0.0165        0.248        0.312
## 
##                 TNM_CLIN_N=c2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    814     256    0.769  0.0127        0.744        0.794
##    24    550     181    0.590  0.0152        0.561        0.621
##    36    383      89    0.486  0.0160        0.456        0.519
##    48    286      43    0.427  0.0164        0.396        0.461
##    60    217      26    0.386  0.0167        0.354        0.420
##   120     46      34    0.301  0.0194        0.265        0.341
## 
##                 TNM_CLIN_N=c2A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    367      29    0.929  0.0128        0.904        0.954
##    24    286      62    0.767  0.0214        0.726        0.810
##    36    231      36    0.666  0.0244        0.620        0.716
##    48    200      13    0.627  0.0252        0.580        0.679
##    60    162      17    0.571  0.0264        0.522        0.626
##   120     26      27    0.411  0.0371        0.344        0.490
## 
##                 TNM_CLIN_N=c2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    576     199    0.748  0.0155        0.719        0.779
##    24    394     138    0.562  0.0180        0.528        0.598
##    36    287      73    0.454  0.0185        0.419        0.491
##    48    216      36    0.393  0.0186        0.358        0.431
##    60    160      16    0.361  0.0187        0.326        0.399
##   120     25      25    0.264  0.0232        0.222        0.314
## 
##                 TNM_CLIN_N=c2C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    687     155    0.822  0.0130        0.797        0.848
##    24    482     141    0.646  0.0167        0.614        0.679
##    36    332      82    0.527  0.0181        0.493        0.564
##    48    220      44    0.450  0.0188        0.415        0.489
##    60    153      23    0.396  0.0197        0.359        0.436
##   120     21      33    0.281  0.0231        0.239        0.330
## 
##                 TNM_CLIN_N=c3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1414    1077    0.576 0.00984        0.557        0.595
##    24    851     433    0.391 0.00993        0.372        0.411
##    36    570     154    0.314 0.00973        0.296        0.334
##    48    388      85    0.263 0.00961        0.245        0.283
##    60    279      27    0.243 0.00964        0.225        0.262
##   120     31      45    0.168 0.01312        0.145        0.196
## 
##                 TNM_CLIN_N=cX 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  75826    8985    0.898 0.00102        0.896        0.900
##    24  67601    5322    0.834 0.00127        0.831        0.836
##    36  60464    4113    0.782 0.00143        0.779        0.785
##    48  54421    3020    0.742 0.00153        0.739        0.745
##    60  48736    2439    0.707 0.00161        0.704        0.711
##   120  19157    7130    0.577 0.00195        0.573        0.581
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_N
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_N, data = data)
## 
##   n= 354612, number of events= 88074 
##    (8298 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)    
## TNM_CLIN_Nc0  -0.23062   0.79404  0.04299 -5.365 8.12e-08 ***
## TNM_CLIN_Nc1   1.07883   2.94122  0.04671 23.098  < 2e-16 ***
## TNM_CLIN_Nc1A  0.43514   1.54518  0.06136  7.092 1.32e-12 ***
## TNM_CLIN_Nc1B  1.26262   3.53466  0.05363 23.541  < 2e-16 ***
## TNM_CLIN_Nc2   1.18426   3.26826  0.05845 20.262  < 2e-16 ***
## TNM_CLIN_Nc2A  0.60849   1.83766  0.08487  7.170 7.52e-13 ***
## TNM_CLIN_Nc2B  1.26659   3.54872  0.06231 20.328  < 2e-16 ***
## TNM_CLIN_Nc2C  1.13924   3.12438  0.06259 18.201  < 2e-16 ***
## TNM_CLIN_Nc3   1.76674   5.85177  0.04878 36.216  < 2e-16 ***
## TNM_CLIN_NcX   0.14286   1.15357  0.04308  3.316 0.000913 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## TNM_CLIN_Nc0      0.794     1.2594    0.7299    0.8638
## TNM_CLIN_Nc1      2.941     0.3400    2.6839    3.2232
## TNM_CLIN_Nc1A     1.545     0.6472    1.3701    1.7426
## TNM_CLIN_Nc1B     3.535     0.2829    3.1820    3.9265
## TNM_CLIN_Nc2      3.268     0.3060    2.9145    3.6649
## TNM_CLIN_Nc2A     1.838     0.5442    1.5560    2.1702
## TNM_CLIN_Nc2B     3.549     0.2818    3.1408    4.0096
## TNM_CLIN_Nc2C     3.124     0.3201    2.7637    3.5322
## TNM_CLIN_Nc3      5.852     0.1709    5.3182    6.4389
## TNM_CLIN_NcX      1.154     0.8669    1.0602    1.2552
## 
## Concordance= 0.59  (se = 0.001 )
## Rsquare= 0.033   (max possible= 0.998 )
## Likelihood ratio test= 11826  on 10 df,   p=0
## Wald test            = 15960  on 10 df,   p=0
## Score (logrank) test = 19080  on 10 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_N

Clinical Stage Group

uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)

## _________________________________________________
##    
## ## TNM_CLIN_STAGE_GROUP
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_STAGE_GROUP, data = data)
## 
##    43 observations deleted due to missingness 
##                               n events median 0.95LCL 0.95UCL
## TNM_CLIN_STAGE_GROUP=0     3415    657     NA  157.90      NA
## TNM_CLIN_STAGE_GROUP=1    11136   1745     NA  161.58      NA
## TNM_CLIN_STAGE_GROUP=1A   95569  10889     NA      NA      NA
## TNM_CLIN_STAGE_GROUP=1B   63589   9853     NA  164.44      NA
## TNM_CLIN_STAGE_GROUP=2     1978    608 151.26  117.19      NA
## TNM_CLIN_STAGE_GROUP=2A   22432   6304 118.54  113.81   123.4
## TNM_CLIN_STAGE_GROUP=2B   15534   6087  76.16   73.53    79.2
## TNM_CLIN_STAGE_GROUP=2C    7948   4304  42.45   40.54    43.9
## TNM_CLIN_STAGE_GROUP=3    11303   5144  61.27   58.78    64.8
## TNM_CLIN_STAGE_GROUP=4    13516  11322   6.41    6.18     6.6
## TNM_CLIN_STAGE_GROUP=N_A   1609    548 160.33  150.70      NA
## TNM_CLIN_STAGE_GROUP=99  114838  32497 164.47  161.68      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_STAGE_GROUP, data = data)
## 
## 43 observations deleted due to missingness 
##                 TNM_CLIN_STAGE_GROUP=0 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2944     109    0.966 0.00319        0.960        0.972
##    24   2494     122    0.924 0.00484        0.914        0.933
##    36   2032     101    0.883 0.00609        0.871        0.895
##    48   1633      88    0.842 0.00724        0.828        0.856
##    60   1294      64    0.806 0.00822        0.790        0.822
##   120    251     159    0.639 0.01461        0.611        0.668
## 
##                 TNM_CLIN_STAGE_GROUP=1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9791     201    0.981 0.00134        0.978        0.983
##    24   8870     279    0.952 0.00215        0.948        0.956
##    36   7747     282    0.920 0.00279        0.915        0.926
##    48   6595     210    0.893 0.00326        0.887        0.900
##    60   5348     216    0.862 0.00380        0.854        0.869
##   120   1070     504    0.726 0.00695        0.712        0.739
## 
##                 TNM_CLIN_STAGE_GROUP=1A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  82128    1263    0.986 0.000400        0.985        0.986
##    24  72641    1639    0.965 0.000638        0.964        0.966
##    36  61084    1654    0.941 0.000848        0.940        0.943
##    48  50407    1480    0.917 0.001042        0.915        0.919
##    60  40560    1201    0.893 0.001225        0.890        0.895
##   120   7811    3257    0.766 0.002532        0.761        0.771
## 
##                 TNM_CLIN_STAGE_GROUP=1B 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  54788    1190    0.980 0.000579        0.979        0.981
##    24  47096    1807    0.946 0.000969        0.944        0.948
##    36  37773    1718    0.908 0.001290        0.905        0.910
##    48  29886    1417    0.870 0.001576        0.867        0.874
##    60  22806    1149    0.833 0.001857        0.829        0.837
##   120   3361    2356    0.670 0.003792        0.663        0.677
## 
##                 TNM_CLIN_STAGE_GROUP=2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1705     102    0.946 0.00523        0.936        0.956
##    24   1471     142    0.865 0.00807        0.849        0.881
##    36   1244     112    0.796 0.00969        0.778        0.815
##    48   1027      77    0.744 0.01073        0.723        0.765
##    60    847      54    0.702 0.01154        0.680        0.725
##   120     90     117    0.535 0.01850        0.500        0.572
## 
##                 TNM_CLIN_STAGE_GROUP=2A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  19406     813    0.961 0.00133        0.959        0.964
##    24  16245    1384    0.890 0.00223        0.885        0.894
##    36  12639    1262    0.815 0.00288        0.809        0.820
##    48   9653     943    0.748 0.00336        0.742        0.755
##    60   7206     668    0.691 0.00376        0.684        0.699
##   120   1006    1143    0.496 0.00632        0.484        0.509
## 
##                 TNM_CLIN_STAGE_GROUP=2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  13273     965    0.935 0.00204        0.931        0.939
##    24  10494    1611    0.816 0.00329        0.809        0.822
##    36   7801    1267    0.710 0.00400        0.702        0.718
##    48   5804     800    0.631 0.00443        0.622        0.639
##    60   4177     544    0.565 0.00478        0.556        0.575
##   120    528     841    0.368 0.00712        0.354        0.382
## 
##                 TNM_CLIN_STAGE_GROUP=2C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6406    1045    0.863 0.00393        0.856        0.871
##    24   4562    1299    0.681 0.00547        0.670        0.691
##    36   3142     831    0.548 0.00605        0.536        0.560
##    48   2173     473    0.457 0.00634        0.445        0.470
##    60   1524     246    0.400 0.00651        0.387        0.413
##   120    148     399    0.220 0.00889        0.203        0.238
## 
##                 TNM_CLIN_STAGE_GROUP=3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9131    1454    0.866 0.00327        0.860        0.873
##    24   6869    1523    0.716 0.00443        0.707        0.725
##    36   5137     884    0.617 0.00491        0.608        0.627
##    48   3910     532    0.549 0.00519        0.539        0.559
##    60   2945     283    0.505 0.00539        0.495        0.516
##   120    465     439    0.379 0.00721        0.365        0.393
## 
##                 TNM_CLIN_STAGE_GROUP=4 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4103    8748   0.3321 0.00415       0.3241       0.3403
##    24   2140    1603   0.1967 0.00359       0.1898       0.2039
##    36   1227     547   0.1411 0.00328       0.1348       0.1477
##    48    804     199   0.1159 0.00315       0.1099       0.1223
##    60    559      81   0.1027 0.00312       0.0968       0.1090
##   120     82     130   0.0663 0.00353       0.0598       0.0736
## 
##                 TNM_CLIN_STAGE_GROUP=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1406     113    0.928 0.00655        0.915        0.941
##    24   1275      98    0.862 0.00882        0.845        0.880
##    36   1171      67    0.816 0.00998        0.797        0.836
##    48   1074      61    0.773 0.01088        0.752        0.795
##    60    987      40    0.744 0.01142        0.722        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_CLIN_STAGE_GROUP=99 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  99043    5937    0.946 0.000687        0.944        0.947
##    24  86876    6115    0.885 0.000985        0.883        0.887
##    36  75066    4926    0.833 0.001176        0.831        0.835
##    48  65153    3668    0.790 0.001309        0.788        0.793
##    60  56039    2862    0.754 0.001416        0.751        0.757
##   120  19360    7703    0.614 0.001893        0.610        0.617
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_STAGE_GROUP
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_CLIN_STAGE_GROUP, data = data)
## 
##   n= 362867, number of events= 89958 
##    (43 observations deleted due to missingness)
## 
##                             coef exp(coef) se(coef)       z Pr(>|z|)    
## TNM_CLIN_STAGE_GROUP1   -0.34493   0.70827  0.04577  -7.535 4.86e-14 ***
## TNM_CLIN_STAGE_GROUP1A  -0.58497   0.55712  0.04017 -14.561  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP1B  -0.18149   0.83403  0.04029  -4.504 6.67e-06 ***
## TNM_CLIN_STAGE_GROUP1C        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP2    0.43921   1.55148  0.05628   7.805 6.00e-15 ***
## TNM_CLIN_STAGE_GROUP2A   0.46369   1.58992  0.04100  11.310  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP2B   0.88353   2.41942  0.04107  21.513  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP2C   1.38281   3.98610  0.04190  33.004  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP3    1.08099   2.94759  0.04143  26.089  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP3A        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP3B        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP3C        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP4    2.83685  17.06185  0.04021  70.552  < 2e-16 ***
## TNM_CLIN_STAGE_GROUP4A        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP4B        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUP4C        NA        NA  0.00000      NA       NA    
## TNM_CLIN_STAGE_GROUPN_A  0.22022   1.24635  0.05787   3.805 0.000142 ***
## TNM_CLIN_STAGE_GROUP99   0.20140   1.22311  0.03942   5.110 3.23e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## TNM_CLIN_STAGE_GROUP1      0.7083    1.41189    0.6475    0.7748
## TNM_CLIN_STAGE_GROUP1A     0.5571    1.79494    0.5149    0.6028
## TNM_CLIN_STAGE_GROUP1B     0.8340    1.19900    0.7707    0.9026
## TNM_CLIN_STAGE_GROUP1C         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP2      1.5515    0.64454    1.3895    1.7324
## TNM_CLIN_STAGE_GROUP2A     1.5899    0.62896    1.4672    1.7230
## TNM_CLIN_STAGE_GROUP2B     2.4194    0.41332    2.2323    2.6222
## TNM_CLIN_STAGE_GROUP2C     3.9861    0.25087    3.6718    4.3273
## TNM_CLIN_STAGE_GROUP3      2.9476    0.33926    2.7177    3.1969
## TNM_CLIN_STAGE_GROUP3A         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP3B         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP3C         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP4     17.0619    0.05861   15.7689   18.4609
## TNM_CLIN_STAGE_GROUP4A         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP4B         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUP4C         NA         NA        NA        NA
## TNM_CLIN_STAGE_GROUPN_A    1.2464    0.80234    1.1127    1.3960
## TNM_CLIN_STAGE_GROUP99     1.2231    0.81759    1.1322    1.3213
## 
## Concordance= 0.705  (se = 0.001 )
## Rsquare= 0.149   (max possible= 0.998 )
## Likelihood ratio test= 58580  on 11 df,   p=0
## Wald test            = 83674  on 11 df,   p=0
## Score (logrank) test = 142755  on 11 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_STAGE_GROUP

Pathologic T Stage

uni_var(test_var = "TNM_PATH_T", data_imp = data)

## _________________________________________________
##    
## ## TNM_PATH_T
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_T, data = data)
## 
##    15174 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## TNM_PATH_T=N_A   1608    548  160.3   150.7      NA
## TNM_PATH_T=p0    4729    966     NA   151.8      NA
## TNM_PATH_T=p1   12061   1818     NA   161.4      NA
## TNM_PATH_T=p1A  60788   4622     NA    95.5      NA
## TNM_PATH_T=p1B  23363   2046   97.1      NA      NA
## TNM_PATH_T=p2    3707    902     NA   156.0      NA
## TNM_PATH_T=p2A  40964   6867  164.7   164.4      NA
## TNM_PATH_T=p2B   9316   2469  130.2   119.3   133.8
## TNM_PATH_T=p3    2393    906   97.6    87.9   113.3
## TNM_PATH_T=p3A  17370   4845  120.4   116.7   127.2
## TNM_PATH_T=p3B  12918   5054   73.5    70.7    77.7
## TNM_PATH_T=p4    1628    874   45.3    42.2    50.5
## TNM_PATH_T=p4A   9301   3754   69.8    66.8    73.7
## TNM_PATH_T=p4B  15277   8722   34.9    33.9    35.8
## TNM_PATH_T=pIS   1358    221     NA   151.2      NA
## TNM_PATH_T=pX  130955  40263  165.4   164.6      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_T, data = data)
## 
## 15174 observations deleted due to missingness 
##                 TNM_PATH_T=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1405     113    0.928 0.00655        0.915        0.941
##    24   1274      98    0.862 0.00883        0.845        0.880
##    36   1170      67    0.816 0.00998        0.797        0.836
##    48   1073      61    0.773 0.01089        0.752        0.794
##    60    986      40    0.743 0.01142        0.721        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_PATH_T=p0 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3692     425    0.905 0.00439        0.896        0.914
##    24   2872     212    0.849 0.00555        0.838        0.860
##    36   2069     123    0.808 0.00641        0.796        0.821
##    48   1491      75    0.775 0.00721        0.761        0.789
##    60    984      50    0.744 0.00818        0.728        0.760
##   120    106      76    0.616 0.01797        0.582        0.652
## 
##                 TNM_PATH_T=p1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  10570     226    0.980 0.00132        0.977        0.983
##    24   9589     292    0.952 0.00206        0.948        0.956
##    36   8462     272    0.924 0.00262        0.919        0.929
##    48   7173     226    0.897 0.00308        0.891        0.903
##    60   5859     225    0.867 0.00359        0.860        0.874
##   120   1282     509    0.733 0.00672        0.720        0.746
## 
##                 TNM_PATH_T=p1A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  50621     847    0.985 0.000525        0.984        0.986
##    24  41969    1022    0.963 0.000839        0.962        0.965
##    36  31134     926    0.939 0.001136        0.937        0.941
##    48  21579     758    0.912 0.001470        0.909        0.915
##    60  13362     542    0.884 0.001871        0.880        0.887
## 
##                 TNM_PATH_T=p1B 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  19493     383    0.982 0.000912        0.980        0.984
##    24  15895     494    0.955 0.001490        0.952        0.958
##    36  11326     457    0.923 0.002056        0.919        0.927
##    48   7557     319    0.892 0.002628        0.887        0.897
##    60   4426     199    0.863 0.003294        0.856        0.869
## 
##                 TNM_PATH_T=p2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3243     119    0.966 0.00307        0.960        0.972
##    24   2887     162    0.916 0.00481        0.907        0.925
##    36   2540     133    0.872 0.00589        0.861        0.884
##    48   2155     129    0.825 0.00687        0.812        0.839
##    60   1810      95    0.787 0.00761        0.772        0.802
##   120    379     242    0.617 0.01216        0.594        0.641
## 
##                 TNM_PATH_T=p2A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  35584     816    0.979 0.000739        0.977        0.980
##    24  30537    1263    0.942 0.001240        0.940        0.944
##    36  24466    1213    0.901 0.001651        0.898        0.904
##    48  19261     993    0.861 0.002012        0.857        0.865
##    60  14662     774    0.822 0.002357        0.818        0.827
##   120   2789    1619    0.659 0.004441        0.650        0.668
## 
##                 TNM_PATH_T=p2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7937     385    0.956 0.00221        0.951        0.960
##    24   6614     519    0.890 0.00346        0.883        0.897
##    36   5091     508    0.815 0.00449        0.806        0.824
##    48   3855     357    0.752 0.00525        0.742        0.763
##    60   2830     225    0.703 0.00584        0.692        0.715
##   120    494     426    0.516 0.00968        0.498        0.536
## 
##                 TNM_PATH_T=p3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2050     149    0.935 0.00518        0.925        0.945
##    24   1754     187    0.847 0.00769        0.832        0.862
##    36   1426     180    0.757 0.00938        0.738        0.775
##    48   1168     118    0.691 0.01035        0.671        0.711
##    60    905     108    0.622 0.01125        0.600        0.644
##   120    170     155    0.453 0.01541        0.424        0.484
## 
##                 TNM_PATH_T=p3A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  15063     643    0.961 0.00152        0.958        0.964
##    24  12546    1081    0.889 0.00254        0.884        0.894
##    36   9650     968    0.814 0.00327        0.807        0.820
##    48   7382     689    0.750 0.00381        0.743        0.758
##    60   5437     515    0.692 0.00430        0.684        0.700
##   120   1026     872    0.502 0.00686        0.489        0.515
## 
##                 TNM_PATH_T=p3B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  11009     866    0.930 0.00231        0.925        0.934
##    24   8564    1397    0.806 0.00368        0.799        0.813
##    36   6167    1106    0.693 0.00448        0.684        0.702
##    48   4485     613    0.617 0.00492        0.608        0.627
##    60   3163     405    0.555 0.00532        0.544        0.565
##   120    512     619    0.369 0.00776        0.354        0.385
## 
##                 TNM_PATH_T=p4 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1274     264    0.833 0.00941        0.814        0.851
##    24    967     218    0.685 0.01193        0.662        0.709
##    36    747     143    0.580 0.01296        0.555        0.606
##    48    556     117    0.484 0.01351        0.458        0.511
##    60    421      59    0.428 0.01378        0.402        0.456
##   120     74      65    0.313 0.01702        0.282        0.348
## 
##                 TNM_PATH_T=p4A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7768     793    0.911 0.00303        0.905        0.917
##    24   5923    1064    0.779 0.00454        0.771        0.788
##    36   4366     706    0.679 0.00531        0.669        0.689
##    48   3217     452    0.602 0.00581        0.591        0.614
##    60   2294     285    0.542 0.00623        0.530        0.555
##   120    399     429    0.374 0.00873        0.357        0.392
## 
##                 TNM_PATH_T=p4B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  11675    2691    0.817 0.00319        0.811        0.824
##    24   7938    2677    0.621 0.00411        0.613        0.629
##    36   5215    1542    0.490 0.00440        0.482        0.499
##    48   3501     788    0.408 0.00454        0.399        0.417
##    60   2366     415    0.354 0.00466        0.345        0.363
##   120    298     578    0.208 0.00610        0.197        0.220
## 
##                 TNM_PATH_T=pIS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1160      22    0.982 0.00372        0.975        0.990
##    24    990      37    0.949 0.00647        0.937        0.962
##    36    777      42    0.904 0.00921        0.886        0.922
##    48    612      31    0.865 0.01118        0.843        0.887
##    60    459      22    0.829 0.01303        0.804        0.855
##   120    101      64    0.632 0.02541        0.584        0.684
## 
##                 TNM_PATH_T=pX 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 111129   10285    0.919 0.000770        0.917        0.920
##    24 102135    5970    0.868 0.000963        0.867        0.870
##    36  94648    4783    0.827 0.001087        0.825        0.829
##    48  87842    3875    0.793 0.001174        0.791        0.795
##    60  80833    3231    0.763 0.001241        0.761        0.765
##   120  26542   10481    0.634 0.001591        0.631        0.637
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_PATH_T
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_T, data = data)
## 
##   n= 347736, number of events= 84877 
##    (15174 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)       z Pr(>|z|)    
## TNM_PATH_Tp0   0.11831   1.12559  0.05353   2.210 0.027086 *  
## TNM_PATH_Tp1  -0.61500   0.54064  0.04875 -12.616  < 2e-16 ***
## TNM_PATH_Tp1A -0.90762   0.40348  0.04526 -20.054  < 2e-16 ***
## TNM_PATH_Tp1B -0.73029   0.48177  0.04818 -15.157  < 2e-16 ***
## TNM_PATH_Tp2  -0.13147   0.87680  0.05417  -2.427 0.015228 *  
## TNM_PATH_Tp2A -0.36182   0.69641  0.04442  -8.146 3.33e-16 ***
## TNM_PATH_Tp2B  0.17705   1.19368  0.04725   3.747 0.000179 ***
## TNM_PATH_Tp3   0.44268   1.55688  0.05414   8.177 3.33e-16 ***
## TNM_PATH_Tp3A  0.20590   1.22863  0.04510   4.565 4.99e-06 ***
## TNM_PATH_Tp3B  0.65706   1.92910  0.04502  14.595  < 2e-16 ***
## TNM_PATH_Tp4   1.00597   2.73456  0.05452  18.452  < 2e-16 ***
## TNM_PATH_Tp4A  0.70111   2.01599  0.04577  15.317  < 2e-16 ***
## TNM_PATH_Tp4B  1.27041   3.56231  0.04411  28.800  < 2e-16 ***
## TNM_PATH_TpIS -0.37452   0.68762  0.07970  -4.699 2.61e-06 ***
## TNM_PATH_TpX  -0.06880   0.93351  0.04301  -1.600 0.109646    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## TNM_PATH_Tp0     1.1256     0.8884    1.0135    1.2501
## TNM_PATH_Tp1     0.5406     1.8497    0.4914    0.5948
## TNM_PATH_Tp1A    0.4035     2.4784    0.3692    0.4409
## TNM_PATH_Tp1B    0.4818     2.0757    0.4384    0.5295
## TNM_PATH_Tp2     0.8768     1.1405    0.7885    0.9750
## TNM_PATH_Tp2A    0.6964     1.4359    0.6383    0.7598
## TNM_PATH_Tp2B    1.1937     0.8377    1.0881    1.3095
## TNM_PATH_Tp3     1.5569     0.6423    1.4001    1.7311
## TNM_PATH_Tp3A    1.2286     0.8139    1.1247    1.3422
## TNM_PATH_Tp3B    1.9291     0.5184    1.7662    2.1071
## TNM_PATH_Tp4     2.7346     0.3657    2.4574    3.0429
## TNM_PATH_Tp4A    2.0160     0.4960    1.8430    2.2052
## TNM_PATH_Tp4B    3.5623     0.2807    3.2673    3.8840
## TNM_PATH_TpIS    0.6876     1.4543    0.5882    0.8039
## TNM_PATH_TpX     0.9335     1.0712    0.8580    1.0156
## 
## Concordance= 0.641  (se = 0.001 )
## Rsquare= 0.063   (max possible= 0.997 )
## Likelihood ratio test= 22560  on 15 df,   p=0
## Wald test            = 25245  on 15 df,   p=0
## Score (logrank) test = 29762  on 15 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_T

Pathologic N Stage

uni_var(test_var = "TNM_PATH_N", data_imp = data)

## _________________________________________________
##    
## ## TNM_PATH_N
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_N, data = data)
## 
##    28869 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## TNM_PATH_N=N_A   1608    548  160.3   150.7      NA
## TNM_PATH_N=p0  161766  28438     NA   165.4      NA
## TNM_PATH_N=p1    4601   1864   85.2    76.4    94.5
## TNM_PATH_N=p1A  10736   2992  134.1   127.5   148.5
## TNM_PATH_N=p1B   2516   1177   49.1    43.1    55.5
## TNM_PATH_N=p2    1370    679   50.8    45.8    59.0
## TNM_PATH_N=p2A   3523   1289   79.7    73.7    91.1
## TNM_PATH_N=p2B   1858    913   41.5    37.5    47.4
## TNM_PATH_N=p2C   1798    860   48.8    43.7    54.3
## TNM_PATH_N=p3    4889   3056   25.0    23.9    26.2
## TNM_PATH_N=pX  139376  41237  161.3   159.6   164.5
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_N, data = data)
## 
## 28869 observations deleted due to missingness 
##                 TNM_PATH_N=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1405     113    0.928 0.00655        0.915        0.941
##    24   1274      98    0.862 0.00883        0.845        0.880
##    36   1170      67    0.816 0.00998        0.797        0.836
##    48   1073      61    0.773 0.01089        0.752        0.794
##    60    986      40    0.743 0.01142        0.721        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_PATH_N=p0 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 139818    3568    0.976 0.000392        0.976        0.977
##    24 121377    5311    0.937 0.000645        0.936        0.939
##    36  99735    5080    0.895 0.000846        0.893        0.897
##    48  80892    3840    0.858 0.001004        0.856        0.860
##    60  63553    3057    0.822 0.001152        0.820        0.824
##   120  13706    6754    0.674 0.002023        0.670        0.677
## 
##                 TNM_PATH_N=p1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3778     490    0.889 0.00474        0.880        0.898
##    24   3001     479    0.772 0.00647        0.759        0.784
##    36   2337     353    0.676 0.00741        0.662        0.691
##    48   1832     210    0.612 0.00793        0.596        0.627
##    60   1380     128    0.564 0.00837        0.548        0.580
##   120    247     183    0.437 0.01134        0.415        0.459
## 
##                 TNM_PATH_N=p1A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9435     407    0.960 0.00194        0.956        0.964
##    24   7696     796    0.875 0.00338        0.868        0.882
##    36   5840     632    0.797 0.00429        0.788        0.805
##    48   4401     437    0.731 0.00495        0.722        0.741
##    60   3208     279    0.680 0.00549        0.669        0.691
##   120    595     405    0.529 0.00857        0.512        0.546
## 
##                 TNM_PATH_N=p1B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1945     381    0.842 0.00746        0.827        0.856
##    24   1400     365    0.676 0.00982        0.657        0.696
##    36    968     216    0.563 0.01079        0.542        0.585
##    48    692      92    0.504 0.01131        0.482        0.526
##    60    490      52    0.461 0.01181        0.438        0.485
##   120     85      65    0.354 0.01574        0.324        0.386
## 
##                 TNM_PATH_N=p2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1121     168    0.873 0.00915        0.855        0.891
##    24    834     199    0.712 0.01274        0.688        0.738
##    36    635     121    0.604 0.01411        0.577        0.632
##    48    475      90    0.514 0.01489        0.485        0.544
##    60    366      43    0.463 0.01529        0.434        0.494
##   120     75      48    0.364 0.01879        0.329        0.402
## 
##                 TNM_PATH_N=p2A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3059     188    0.944 0.00398        0.936        0.952
##    24   2377     392    0.817 0.00690        0.804        0.831
##    36   1700     293    0.707 0.00846        0.691        0.724
##    48   1247     166    0.632 0.00936        0.614        0.651
##    60    890      95    0.579 0.01007        0.559        0.599
##   120    162     149    0.413 0.01495        0.385        0.444
## 
##                 TNM_PATH_N=p2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1442     287    0.840 0.00867        0.823        0.857
##    24    991     305    0.653 0.01161        0.631        0.677
##    36    668     165    0.535 0.01268        0.511        0.560
##    48    463      74    0.469 0.01325        0.444        0.496
##    60    320      33    0.430 0.01377        0.404        0.458
##   120     61      43    0.329 0.01833        0.295        0.367
## 
##                 TNM_PATH_N=p2C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1427     227    0.868 0.00817        0.852        0.884
##    24   1004     275    0.692 0.01151        0.670        0.715
##    36    693     144    0.583 0.01281        0.559        0.609
##    48    479      87    0.503 0.01367        0.477        0.530
##    60    337      51    0.443 0.01438        0.416        0.473
##   120     58      73    0.287 0.01891        0.253        0.327
## 
##                 TNM_PATH_N=p3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3417    1294    0.730 0.00643        0.717        0.742
##    24   2133     970    0.513 0.00740        0.498        0.527
##    36   1404     407    0.407 0.00752        0.393        0.422
##    48    930     191    0.345 0.00761        0.330        0.360
##    60    656      81    0.311 0.00774        0.297        0.327
##   120    100     102    0.231 0.00959        0.213        0.250
## 
##                 TNM_PATH_N=pX 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 115671   11418    0.914 0.000766        0.913        0.916
##    24 101946    6977    0.858 0.000975        0.856        0.860
##    36  88685    5346    0.811 0.001113        0.809        0.813
##    48  77042    4131    0.771 0.001217        0.769        0.774
##    60  66676    3203    0.738 0.001301        0.735        0.740
##   120  19083    8917    0.600 0.001758        0.596        0.603
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_PATH_N
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_N, data = data)
## 
##   n= 334041, number of events= 83053 
##    (28869 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)    
## TNM_PATH_Np0  -0.35771   0.69927  0.04315 -8.290  < 2e-16 ***
## TNM_PATH_Np1   0.63924   1.89504  0.04862 13.147  < 2e-16 ***
## TNM_PATH_Np1A  0.22139   1.24781  0.04650  4.761 1.92e-06 ***
## TNM_PATH_Np1B  0.98521   2.67836  0.05176 19.034  < 2e-16 ***
## TNM_PATH_Np2   0.89237   2.44091  0.05745 15.533  < 2e-16 ***
## TNM_PATH_Np2A  0.57084   1.76975  0.05103 11.186  < 2e-16 ***
## TNM_PATH_Np2B  1.06589   2.90343  0.05409 19.708  < 2e-16 ***
## TNM_PATH_Np2C  1.00466   2.73098  0.05470 18.365  < 2e-16 ***
## TNM_PATH_Np3   1.46776   4.33951  0.04646 31.591  < 2e-16 ***
## TNM_PATH_NpX   0.05819   1.05991  0.04301  1.353    0.176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## TNM_PATH_Np0     0.6993     1.4301    0.6426     0.761
## TNM_PATH_Np1     1.8950     0.5277    1.7228     2.085
## TNM_PATH_Np1A    1.2478     0.8014    1.1391     1.367
## TNM_PATH_Np1B    2.6784     0.3734    2.4200     2.964
## TNM_PATH_Np2     2.4409     0.4097    2.1810     2.732
## TNM_PATH_Np2A    1.7698     0.5651    1.6013     1.956
## TNM_PATH_Np2B    2.9034     0.3444    2.6114     3.228
## TNM_PATH_Np2C    2.7310     0.3662    2.4533     3.040
## TNM_PATH_Np3     4.3395     0.2304    3.9618     4.753
## TNM_PATH_NpX     1.0599     0.9435    0.9742     1.153
## 
## Concordance= 0.604  (se = 0.001 )
## Rsquare= 0.036   (max possible= 0.998 )
## Likelihood ratio test= 12120  on 10 df,   p=0
## Wald test            = 15069  on 10 df,   p=0
## Score (logrank) test = 17350  on 10 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_N

Pathologic M Stage

uni_var(test_var = "TNM_PATH_M", data_imp = data)

## _________________________________________________
##    
## ## TNM_PATH_M
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_M, data = data)
## 
##    199448 observations deleted due to missingness 
##                     n events median 0.95LCL 0.95UCL
## TNM_PATH_M=N_A   1609    549 160.33  150.70      NA
## TNM_PATH_M=p1    1989   1731   7.43    6.97    8.08
## TNM_PATH_M=p1A   1165    718  23.13   20.50   26.41
## TNM_PATH_M=p1B    907    704  10.55    9.43   12.12
## TNM_PATH_M=p1C   2241   1898   5.88    5.49    6.34
## TNM_PATH_M=pX  155551  49114 165.19  164.47      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_M, data = data)
## 
## 199448 observations deleted due to missingness 
##                 TNM_PATH_M=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1406     113    0.928 0.00655        0.915        0.941
##    24   1275      98    0.862 0.00882        0.845        0.880
##    36   1171      67    0.816 0.00998        0.797        0.836
##    48   1074      61    0.773 0.01088        0.752        0.795
##    60    986      41    0.743 0.01143        0.721        0.766
##   120    400     138    0.619 0.01375        0.592        0.646
## 
##                 TNM_PATH_M=p1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    669    1261   0.3534 0.01087       0.3327       0.3753
##    24    379     265   0.2105 0.00938       0.1929       0.2297
##    36    260      98   0.1546 0.00842       0.1389       0.1720
##    48    205      39   0.1305 0.00795       0.1158       0.1470
##    60    172      20   0.1173 0.00767       0.1032       0.1334
##   120     43      41   0.0833 0.00725       0.0702       0.0988
## 
##                 TNM_PATH_M=p1A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    711     362    0.675  0.0141        0.647        0.703
##    24    466     184    0.492  0.0154        0.463        0.523
##    36    315      89    0.389  0.0156        0.360        0.421
##    48    239      33    0.345  0.0156        0.316        0.377
##    60    191      13    0.325  0.0157        0.295        0.357
##   120     39      34    0.231  0.0187        0.197        0.271
## 
##                 TNM_PATH_M=p1B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    396     468    0.470  0.0169       0.4376        0.504
##    24    215     138    0.299  0.0159       0.2691        0.331
##    36    107      59    0.206  0.0149       0.1788        0.237
##    48     65      20    0.163  0.0146       0.1364        0.194
##    60     41      10    0.134  0.0146       0.1087        0.166
##   120      7       7    0.102  0.0155       0.0759        0.138
## 
##                 TNM_PATH_M=p1C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    610    1536   0.2964 0.00986       0.2777       0.3164
##    24    289     246   0.1695 0.00836       0.1539       0.1867
##    36    154      71   0.1217 0.00773       0.1075       0.1379
##    48     96      21   0.1036 0.00753       0.0899       0.1195
##    60     59      13   0.0880 0.00756       0.0743       0.1041
##   120      4      11   0.0607 0.00900       0.0454       0.0811
## 
##                 TNM_PATH_M=pX 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 137506    7936    0.947 0.000578        0.946        0.948
##    24 126905    7769    0.893 0.000808        0.891        0.895
##    36 117977    6697    0.845 0.000951        0.844        0.847
##    48 110012    5434    0.806 0.001046        0.804        0.808
##    60 101841    4589    0.772 0.001117        0.770        0.774
##   120  34079   14529    0.631 0.001437        0.628        0.634
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_PATH_M
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_M, data = data)
## 
##   n= 163462, number of events= 54714 
##    (199448 observations deleted due to missingness)
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)    
## TNM_PATH_Mp1   2.28750   9.85031  0.04910 46.590   <2e-16 ***
## TNM_PATH_Mp1A  1.44157   4.22733  0.05676 25.400   <2e-16 ***
## TNM_PATH_Mp1B  2.18953   8.93099  0.05710 38.346   <2e-16 ***
## TNM_PATH_Mp1C  2.64046  14.01966  0.04874 54.173   <2e-16 ***
## TNM_PATH_MpX  -0.09322   0.91099  0.04292 -2.172   0.0298 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## TNM_PATH_Mp1      9.850    0.10152    8.9466   10.8453
## TNM_PATH_Mp1A     4.227    0.23656    3.7823    4.7247
## TNM_PATH_Mp1B     8.931    0.11197    7.9854    9.9885
## TNM_PATH_Mp1C    14.020    0.07133   12.7423   15.4250
## TNM_PATH_MpX      0.911    1.09771    0.8375    0.9909
## 
## Concordance= 0.554  (se = 0 )
## Rsquare= 0.083   (max possible= 1 )
## Likelihood ratio test= 14212  on 5 df,   p=0
## Wald test            = 24732  on 5 df,   p=0
## Score (logrank) test = 40425  on 5 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_M

Pathologic Stage Group

uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)

## _________________________________________________
##    
## ## TNM_PATH_STAGE_GROUP
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_STAGE_GROUP, data = data)
## 
##    11146 observations deleted due to missingness 
##                              n events median 0.95LCL 0.95UCL
## TNM_PATH_STAGE_GROUP=0    2692    446     NA  148.21      NA
## TNM_PATH_STAGE_GROUP=1    8007   1254     NA      NA      NA
## TNM_PATH_STAGE_GROUP=1A  86795  10430     NA      NA      NA
## TNM_PATH_STAGE_GROUP=1B  70294  10651     NA      NA      NA
## TNM_PATH_STAGE_GROUP=2    1642    519 150.54  129.41      NA
## TNM_PATH_STAGE_GROUP=2A  24145   6449 135.36  132.04  142.42
## TNM_PATH_STAGE_GROUP=2B  16599   6174  89.26   86.54   92.39
## TNM_PATH_STAGE_GROUP=2C   8258   4301  47.90   46.59   49.71
## TNM_PATH_STAGE_GROUP=3    6964   3123  82.40   76.25   88.18
## TNM_PATH_STAGE_GROUP=3A  10302   2803 154.78  139.93      NA
## TNM_PATH_STAGE_GROUP=3B   9390   3943  67.78   64.76   71.85
## TNM_PATH_STAGE_GROUP=3C   6606   3825  31.38   29.96   32.59
## TNM_PATH_STAGE_GROUP=4    7652   6190   8.51    8.08    8.87
## TNM_PATH_STAGE_GROUP=N_A  1609    548 160.33  150.70      NA
## TNM_PATH_STAGE_GROUP=99  90809  26192 160.03  155.70  161.68
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_STAGE_GROUP, data = data)
## 
## 11146 observations deleted due to missingness 
##                 TNM_PATH_STAGE_GROUP=0 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2302      55    0.978 0.00293        0.972        0.984
##    24   1970      76    0.944 0.00477        0.935        0.953
##    36   1548      71    0.906 0.00640        0.893        0.918
##    48   1255      51    0.874 0.00759        0.859        0.889
##    60    963      46    0.838 0.00893        0.821        0.856
##   120    229     132    0.649 0.01718        0.616        0.683
## 
##                 TNM_PATH_STAGE_GROUP=1 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7088     124    0.984 0.00147        0.981        0.986
##    24   6501     163    0.960 0.00231        0.956        0.965
##    36   5753     179    0.932 0.00303        0.927        0.938
##    48   4996     155    0.906 0.00362        0.899        0.913
##    60   4158     153    0.876 0.00423        0.868        0.884
##   120   1208     414    0.743 0.00733        0.728        0.757
## 
##                 TNM_PATH_STAGE_GROUP=1A 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  75125    1076    0.987 0.000405        0.986        0.987
##    24  67214    1425    0.967 0.000649        0.966        0.968
##    36  57766    1448    0.945 0.000857        0.943        0.947
##    48  48907    1333    0.921 0.001049        0.919        0.924
##    60  40676    1125    0.899 0.001226        0.896        0.901
##   120  10493    3459    0.775 0.002349        0.771        0.780
## 
##                 TNM_PATH_STAGE_GROUP=1B 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12  61234    1018    0.984 0.000483        0.984        0.985
##    24  53754    1595    0.957 0.000816        0.956        0.959
##    36  44573    1750    0.924 0.001116        0.922        0.926
##    48  36505    1459    0.891 0.001368        0.888        0.894
##    60  29141    1277    0.857 0.001617        0.854        0.860
##   120   6648    3134    0.705 0.002983        0.699        0.711
## 
##                 TNM_PATH_STAGE_GROUP=2 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1459      53    0.966 0.00459        0.957        0.975
##    24   1299      97    0.900 0.00774        0.885        0.915
##    36   1128      95    0.833 0.00979        0.814        0.852
##    48    992      60    0.787 0.01090        0.766        0.808
##    60    835      66    0.731 0.01207        0.708        0.755
##   120    194     138    0.552 0.01709        0.519        0.586
## 
##                 TNM_PATH_STAGE_GROUP=2A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  21108     658    0.971 0.00112        0.969        0.973
##    24  18179    1178    0.915 0.00191        0.911        0.918
##    36  14677    1219    0.849 0.00254        0.844        0.854
##    48  11757     938    0.790 0.00300        0.784        0.796
##    60   9236     743    0.736 0.00339        0.729        0.743
##   120   1941    1556    0.541 0.00527        0.530        0.551
## 
##                 TNM_PATH_STAGE_GROUP=2B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  14411     818    0.948 0.00176        0.945        0.952
##    24  11762    1445    0.849 0.00294        0.843        0.855
##    36   8989    1310    0.748 0.00369        0.740        0.755
##    48   6954     833    0.673 0.00413        0.665        0.681
##    60   5273     570    0.613 0.00447        0.604        0.622
##   120   1011    1093    0.416 0.00621        0.404        0.428
## 
##                 TNM_PATH_STAGE_GROUP=2C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6869     843    0.893 0.00347        0.887        0.900
##    24   5068    1242    0.725 0.00515        0.715        0.735
##    36   3608     843    0.596 0.00586        0.585        0.608
##    48   2546     541    0.499 0.00623        0.487        0.511
##    60   1855     297    0.435 0.00644        0.423        0.448
##   120    278     505    0.250 0.00800        0.235        0.266
## 
##                 TNM_PATH_STAGE_GROUP=3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5915     618    0.908 0.00354        0.901        0.915
##    24   4706     884    0.768 0.00526        0.758        0.778
##    36   3784     567    0.672 0.00596        0.660        0.684
##    48   3104     348    0.607 0.00631        0.595        0.620
##    60   2561     223    0.561 0.00655        0.548        0.574
##   120    603     431    0.427 0.00788        0.412        0.443
## 
##                 TNM_PATH_STAGE_GROUP=3A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9201     286    0.971 0.00170        0.968        0.974
##    24   7665     693    0.894 0.00321        0.888        0.901
##    36   6013     586    0.821 0.00415        0.812        0.829
##    48   4701     442    0.756 0.00484        0.746        0.765
##    60   3593     289    0.705 0.00537        0.694        0.715
##   120    754     466    0.554 0.00804        0.539        0.570
## 
##                 TNM_PATH_STAGE_GROUP=3B 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   7993     705    0.921 0.00285        0.916        0.927
##    24   6101    1172    0.780 0.00450        0.771        0.789
##    36   4439     817    0.668 0.00531        0.657        0.678
##    48   3308     461    0.593 0.00575        0.581        0.604
##    60   2367     308    0.532 0.00613        0.520        0.544
##   120    484     447    0.372 0.00817        0.357        0.389
## 
##                 TNM_PATH_STAGE_GROUP=3C 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5036    1266    0.803 0.00496        0.793        0.813
##    24   3296    1284    0.589 0.00630        0.577        0.601
##    36   2166     656    0.462 0.00662        0.450        0.476
##    48   1438     319    0.387 0.00677        0.373        0.400
##    60   1014     124    0.349 0.00690        0.336        0.363
##   120    166     162    0.253 0.00876        0.237        0.271
## 
##                 TNM_PATH_STAGE_GROUP=4 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2857    4448   0.4024 0.00572        0.391        0.414
##    24   1592    1019   0.2529 0.00518        0.243        0.263
##    36    969     385   0.1862 0.00481        0.177        0.196
##    48    673     155   0.1539 0.00463        0.145        0.163
##    60    489      66   0.1375 0.00456        0.129        0.147
##   120    100     104   0.0952 0.00496        0.086        0.105
## 
##                 TNM_PATH_STAGE_GROUP=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1406     113    0.928 0.00655        0.915        0.941
##    24   1275      98    0.862 0.00882        0.845        0.880
##    36   1171      67    0.816 0.00998        0.797        0.836
##    48   1074      61    0.773 0.01088        0.752        0.795
##    60    987      40    0.744 0.01142        0.722        0.766
##   120    400     138    0.619 0.01375        0.593        0.647
## 
##                 TNM_PATH_STAGE_GROUP=99 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  73796    8329    0.904 0.00100        0.902        0.906
##    24  63760    4646    0.845 0.00126        0.842        0.847
##    36  54124    3300    0.799 0.00142        0.796        0.801
##    48  45892    2533    0.759 0.00156        0.756        0.762
##    60  38530    1890    0.726 0.00167        0.722        0.729
##   120  10063    4840    0.590 0.00232        0.586        0.595
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  TNM_PATH_STAGE_GROUP
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ TNM_PATH_STAGE_GROUP, data = data)
## 
##   n= 351764, number of events= 86848 
##    (11146 observations deleted due to missingness)
## 
##                             coef exp(coef) se(coef)      z Pr(>|z|)    
## TNM_PATH_STAGE_GROUP1   -0.27207   0.76180  0.05514 -4.934 8.04e-07 ***
## TNM_PATH_STAGE_GROUP1A  -0.46113   0.63057  0.04836 -9.536  < 2e-16 ***
## TNM_PATH_STAGE_GROUP1B  -0.15970   0.85240  0.04833 -3.304 0.000953 ***
## TNM_PATH_STAGE_GROUP1C        NA        NA  0.00000     NA       NA    
## TNM_PATH_STAGE_GROUP2    0.46330   1.58930  0.06457  7.175 7.22e-13 ***
## TNM_PATH_STAGE_GROUP2A   0.45204   1.57152  0.04896  9.233  < 2e-16 ***
## TNM_PATH_STAGE_GROUP2B   0.87922   2.40902  0.04903 17.931  < 2e-16 ***
## TNM_PATH_STAGE_GROUP2C   1.39818   4.04783  0.04975 28.103  < 2e-16 ***
## TNM_PATH_STAGE_GROUP3    1.00907   2.74306  0.05062 19.934  < 2e-16 ***
## TNM_PATH_STAGE_GROUP3A   0.50918   1.66393  0.05098  9.988  < 2e-16 ***
## TNM_PATH_STAGE_GROUP3B   1.10511   3.01956  0.04996 22.119  < 2e-16 ***
## TNM_PATH_STAGE_GROUP3C   1.69328   5.43730  0.05005 33.831  < 2e-16 ***
## TNM_PATH_STAGE_GROUP4    2.69749  14.84243  0.04907 54.971  < 2e-16 ***
## TNM_PATH_STAGE_GROUP4A        NA        NA  0.00000     NA       NA    
## TNM_PATH_STAGE_GROUP4B        NA        NA  0.00000     NA       NA    
## TNM_PATH_STAGE_GROUP4C        NA        NA  0.00000     NA       NA    
## TNM_PATH_STAGE_GROUPN_A  0.36782   1.44458  0.06379  5.766 8.10e-09 ***
## TNM_PATH_STAGE_GROUP99   0.49614   1.64238  0.04775 10.389  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## TNM_PATH_STAGE_GROUP1      0.7618    1.31268    0.6838    0.8487
## TNM_PATH_STAGE_GROUP1A     0.6306    1.58587    0.5736    0.6933
## TNM_PATH_STAGE_GROUP1B     0.8524    1.17316    0.7754    0.9371
## TNM_PATH_STAGE_GROUP1C         NA         NA        NA        NA
## TNM_PATH_STAGE_GROUP2      1.5893    0.62921    1.4004    1.8037
## TNM_PATH_STAGE_GROUP2A     1.5715    0.63633    1.4277    1.7298
## TNM_PATH_STAGE_GROUP2B     2.4090    0.41511    2.1883    2.6520
## TNM_PATH_STAGE_GROUP2C     4.0478    0.24705    3.6717    4.4624
## TNM_PATH_STAGE_GROUP3      2.7431    0.36456    2.4840    3.0292
## TNM_PATH_STAGE_GROUP3A     1.6639    0.60099    1.5057    1.8388
## TNM_PATH_STAGE_GROUP3B     3.0196    0.33117    2.7379    3.3302
## TNM_PATH_STAGE_GROUP3C     5.4373    0.18391    4.9292    5.9977
## TNM_PATH_STAGE_GROUP4     14.8424    0.06737   13.4814   16.3408
## TNM_PATH_STAGE_GROUP4A         NA         NA        NA        NA
## TNM_PATH_STAGE_GROUP4B         NA         NA        NA        NA
## TNM_PATH_STAGE_GROUP4C         NA         NA        NA        NA
## TNM_PATH_STAGE_GROUPN_A    1.4446    0.69224    1.2748    1.6370
## TNM_PATH_STAGE_GROUP99     1.6424    0.60887    1.4956    1.8035
## 
## Concordance= 0.698  (se = 0.001 )
## Rsquare= 0.116   (max possible= 0.998 )
## Likelihood ratio test= 43484  on 14 df,   p=0
## Wald test            = 54341  on 14 df,   p=0
## Score (logrank) test = 78834  on 14 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_STAGE_GROUP

Margins

uni_var(test_var = "MARGINS", data_imp = data)

## _________________________________________________
##    
## ## MARGINS
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ MARGINS, data = data)
## 
##                                n events median 0.95LCL 0.95UCL
## MARGINS=No Residual       329265  70369     NA  164.73      NA
## MARGINS=Residual, NOS       5326   2401  74.12   68.83    80.5
## MARGINS=Microscopic Resid   5723   2641  68.11   64.89    73.4
## MARGINS=Macroscopic Resid    366    242  23.06   19.25    27.8
## MARGINS=Not evaluable        999    433  82.40   73.66   104.2
## MARGINS=No surg            16733  12252   8.87    8.57     9.2
## MARGINS=Unknown             4498   1634 139.24  129.31   149.4
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ MARGINS, data = data)
## 
##                 MARGINS=No Residual 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 284582   10282    0.967 0.000324        0.966        0.967
##    24 246304   14276    0.916 0.000514        0.915        0.917
##    36 204546   11986    0.868 0.000646        0.867        0.870
##    48 168935    8929    0.828 0.000746        0.826        0.829
##    60 136867    6838    0.791 0.000833        0.790        0.793
##   120  32872   15998    0.646 0.001316        0.643        0.648
## 
##                 MARGINS=Residual, NOS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3980     929    0.818 0.00541        0.807        0.829
##    24   3200     494    0.713 0.00647        0.700        0.725
##    36   2576     316    0.638 0.00702        0.625        0.652
##    48   2032     241    0.575 0.00742        0.561        0.590
##    60   1659     114    0.541 0.00764        0.526        0.556
##   120    346     280    0.399 0.00973        0.381        0.419
## 
##                 MARGINS=Microscopic Resid 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4455     863    0.843 0.00492        0.833        0.853
##    24   3552     597    0.726 0.00615        0.714        0.738
##    36   2817     383    0.643 0.00674        0.630        0.657
##    48   2240     268    0.579 0.00713        0.565        0.593
##    60   1808     168    0.532 0.00740        0.518        0.547
##   120    334     324    0.374 0.00983        0.355        0.394
## 
##                 MARGINS=Macroscopic Resid 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    229     120    0.663  0.0251        0.615        0.714
##    24    149      60    0.481  0.0271        0.431        0.538
##    36    107      25    0.397  0.0271        0.348        0.454
##    48     79      17    0.331  0.0270        0.282        0.388
##    60     63       3    0.317  0.0270        0.268        0.375
##   120      9      14    0.192  0.0330        0.137        0.269
## 
##                 MARGINS=Not evaluable 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    764     162    0.830  0.0122        0.806        0.854
##    24    603     107    0.709  0.0150        0.681        0.739
##    36    499      51    0.646  0.0161        0.616        0.679
##    48    416      25    0.612  0.0166        0.580        0.646
##    60    332      26    0.570  0.0174        0.537        0.605
##   120     82      57    0.430  0.0216        0.390        0.475
## 
##                 MARGINS=No surg 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6526    9055    0.435 0.00394        0.428        0.443
##    24   4167    1743    0.314 0.00377        0.306        0.321
##    36   2879     674    0.259 0.00366        0.252        0.266
##    48   2112     329    0.226 0.00361        0.219        0.234
##    60   1593     168    0.207 0.00361        0.200        0.214
##   120    340     255    0.157 0.00402        0.149        0.165
## 
##                 MARGINS=Unknown 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3621     536    0.875 0.00504        0.866        0.885
##    24   3084     346    0.789 0.00632        0.777        0.802
##    36   2655     216    0.732 0.00697        0.718        0.746
##    48   2320     140    0.692 0.00737        0.677        0.706
##    60   1980      93    0.662 0.00767        0.647        0.677
##   120    589     261    0.536 0.00970        0.517        0.555
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  MARGINS
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ MARGINS, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                              coef exp(coef) se(coef)      z Pr(>|z|)    
## MARGINSResidual, NOS     0.967379  2.631040 0.020756  46.61   <2e-16 ***
## MARGINSMicroscopic Resid 0.985189  2.678317 0.019826  49.69   <2e-16 ***
## MARGINSMacroscopic Resid 1.732810  5.656527 0.064404  26.91   <2e-16 ***
## MARGINSNot evaluable     0.873543  2.395383 0.048205  18.12   <2e-16 ***
## MARGINSNo surg           2.251671  9.503601 0.009911 227.19   <2e-16 ***
## MARGINSUnknown           0.520778  1.683337 0.025027  20.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                          exp(coef) exp(-coef) lower .95 upper .95
## MARGINSResidual, NOS         2.631     0.3801     2.526     2.740
## MARGINSMicroscopic Resid     2.678     0.3734     2.576     2.784
## MARGINSMacroscopic Resid     5.657     0.1768     4.986     6.418
## MARGINSNot evaluable         2.395     0.4175     2.179     2.633
## MARGINSNo surg               9.504     0.1052     9.321     9.690
## MARGINSUnknown               1.683     0.5941     1.603     1.768
## 
## Concordance= 0.612  (se = 0 )
## Rsquare= 0.091   (max possible= 0.998 )
## Likelihood ratio test= 34649  on 6 df,   p=0
## Wald test            = 54125  on 6 df,   p=0
## Score (logrank) test = 78275  on 6 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  MARGINS

Margins Yes/No

#uni_var(test_var = "MARGINS_YN", data_imp = data)

30 Day Readmission

uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)

## _________________________________________________
##    
## ## READM_HOSP_30_DAYS_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ READM_HOSP_30_DAYS_F, data = data)
## 
##                                                 n events median 0.95LCL
## READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 347736  84921    164     162
## READM_HOSP_30_DAYS_F=Unplan_Readmit_Same     3407   1253    113     103
## READM_HOSP_30_DAYS_F=Plan_Readmit_Same       5388   1646    157     140
## READM_HOSP_30_DAYS_F=PlanUnplan_Same          515    135    130     122
## READM_HOSP_30_DAYS_F=9                       5864   2017    160     158
##                                            0.95UCL
## READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit     165
## READM_HOSP_30_DAYS_F=Unplan_Readmit_Same       124
## READM_HOSP_30_DAYS_F=Plan_Readmit_Same          NA
## READM_HOSP_30_DAYS_F=PlanUnplan_Same            NA
## READM_HOSP_30_DAYS_F=9                          NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ READM_HOSP_30_DAYS_F, data = data)
## 
##                 READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 291361   20768    0.937 0.000424        0.936        0.938
##    24 249996   16607    0.881 0.000579        0.880        0.882
##    36 206568   12940    0.833 0.000687        0.831        0.834
##    48 169989    9385    0.792 0.000771        0.790        0.794
##    60 137365    7017    0.757 0.000845        0.755        0.758
##   120  32483   16133    0.616 0.001285        0.613        0.618
## 
##                 READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2713     385    0.882 0.00567        0.871        0.893
##    24   2245     264    0.793 0.00728        0.779        0.807
##    36   1869     156    0.735 0.00810        0.719        0.751
##    48   1536     133    0.679 0.00880        0.662        0.697
##    60   1236      87    0.638 0.00932        0.620        0.657
##   120    303     202    0.484 0.01245        0.460        0.509
## 
##                 READM_HOSP_30_DAYS_F=Plan_Readmit_Same 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4635     280    0.945 0.00320        0.939        0.951
##    24   3988     361    0.869 0.00482        0.860        0.879
##    36   3370     264    0.809 0.00574        0.798        0.820
##    48   2819     195    0.760 0.00639        0.747        0.772
##    60   2327     148    0.717 0.00693        0.703        0.731
##   120    702     348    0.566 0.00934        0.548        0.585
## 
##                 READM_HOSP_30_DAYS_F=PlanUnplan_Same 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    450      30    0.939  0.0107        0.918        0.961
##    24    399      20    0.896  0.0139        0.869        0.924
##    36    315      24    0.837  0.0175        0.803        0.872
##    48    261      20    0.781  0.0203        0.742        0.822
##    60    217      10    0.750  0.0217        0.709        0.794
##   120     24      29    0.573  0.0388        0.502        0.654
## 
##                 READM_HOSP_30_DAYS_F=9 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4998     484    0.914 0.00373        0.907        0.922
##    24   4431     371    0.845 0.00488        0.836        0.855
##    36   3957     267    0.793 0.00553        0.782        0.804
##    48   3529     216    0.748 0.00599        0.737        0.760
##    60   3157     148    0.716 0.00629        0.704        0.728
##   120   1060     477    0.578 0.00779        0.563        0.593
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  READM_HOSP_30_DAYS_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ READM_HOSP_30_DAYS_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                            coef exp(coef) se(coef)      z
## READM_HOSP_30_DAYS_FUnplan_Readmit_Same 0.47735   1.61180  0.02846 16.774
## READM_HOSP_30_DAYS_FPlan_Readmit_Same   0.14940   1.16114  0.02489  6.003
## READM_HOSP_30_DAYS_FPlanUnplan_Same     0.07259   1.07529  0.08614  0.843
## READM_HOSP_30_DAYS_F9                   0.14804   1.15956  0.02254  6.567
##                                         Pr(>|z|)    
## READM_HOSP_30_DAYS_FUnplan_Readmit_Same  < 2e-16 ***
## READM_HOSP_30_DAYS_FPlan_Readmit_Same   1.94e-09 ***
## READM_HOSP_30_DAYS_FPlanUnplan_Same        0.399    
## READM_HOSP_30_DAYS_F9                   5.13e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                         exp(coef) exp(-coef) lower .95
## READM_HOSP_30_DAYS_FUnplan_Readmit_Same     1.612     0.6204    1.5244
## READM_HOSP_30_DAYS_FPlan_Readmit_Same       1.161     0.8612    1.1059
## READM_HOSP_30_DAYS_FPlanUnplan_Same         1.075     0.9300    0.9083
## READM_HOSP_30_DAYS_F9                       1.160     0.8624    1.1094
##                                         upper .95
## READM_HOSP_30_DAYS_FUnplan_Readmit_Same     1.704
## READM_HOSP_30_DAYS_FPlan_Readmit_Same       1.219
## READM_HOSP_30_DAYS_FPlanUnplan_Same         1.273
## READM_HOSP_30_DAYS_F9                       1.212
## 
## Concordance= 0.506  (se = 0 )
## Rsquare= 0.001   (max possible= 0.998 )
## Likelihood ratio test= 311.7  on 4 df,   p=0
## Wald test            = 352.3  on 4 df,   p=0
## Score (logrank) test = 357.7  on 4 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  READM_HOSP_30_DAYS_F

Radiation Type

uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)

## _________________________________________________
##    
## ## RX_SUMM_RADIATION_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_SUMM_RADIATION_F, data = data)
## 
##                                                 n events median 0.95LCL
## RX_SUMM_RADIATION_F=None                   349080  81074  164.7   164.5
## RX_SUMM_RADIATION_F=Beam Radiation          11458   8190   15.5    14.9
## RX_SUMM_RADIATION_F=Radioactive Implants       44     20   64.3    33.7
## RX_SUMM_RADIATION_F=Radioisotopes               9      5   95.3    39.0
## RX_SUMM_RADIATION_F=Beam + Imp or Isotopes      9      6   24.0    16.1
## RX_SUMM_RADIATION_F=Radiation, NOS            114     79   25.7    15.6
## RX_SUMM_RADIATION_F=Unknown                  2196    598     NA   154.8
##                                            0.95UCL
## RX_SUMM_RADIATION_F=None                        NA
## RX_SUMM_RADIATION_F=Beam Radiation            16.2
## RX_SUMM_RADIATION_F=Radioactive Implants        NA
## RX_SUMM_RADIATION_F=Radioisotopes               NA
## RX_SUMM_RADIATION_F=Beam + Imp or Isotopes      NA
## RX_SUMM_RADIATION_F=Radiation, NOS            35.7
## RX_SUMM_RADIATION_F=Unknown                     NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_SUMM_RADIATION_F, data = data)
## 
##                 RX_SUMM_RADIATION_F=None 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 296059   16779    0.949 0.000383        0.948        0.950
##    24 255224   15967    0.896 0.000547        0.895        0.897
##    36 211644   12838    0.848 0.000662        0.846        0.849
##    48 174574    9535    0.807 0.000752        0.805        0.808
##    60 141423    7141    0.771 0.000829        0.769        0.773
##   120  33869   16672    0.628 0.001276        0.625        0.630
## 
##                 RX_SUMM_RADIATION_F=Beam Radiation 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6075    5005    0.557 0.00469        0.548        0.566
##    24   4029    1546    0.409 0.00472        0.400        0.419
##    36   2806     733    0.330 0.00463        0.321        0.339
##    48   2054     359    0.284 0.00458        0.275        0.293
##    60   1507     208    0.253 0.00456        0.244        0.262
##   120    253     312    0.167 0.00542        0.157        0.178
## 
##                 RX_SUMM_RADIATION_F=Radioactive Implants 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     36       6    0.861  0.0525        0.764        0.971
##    24     24       8    0.654  0.0755        0.522        0.820
##    36     17       2    0.589  0.0809        0.450        0.770
##    48     13       0    0.589  0.0809        0.450        0.770
##    60     11       1    0.543  0.0864        0.398        0.742
##   120      2       3    0.264  0.1302        0.100        0.694
## 
##                 RX_SUMM_RADIATION_F=Radioisotopes 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12      7       1    0.889   0.105        0.706            1
##    24      6       1    0.762   0.148        0.521            1
##    36      6       0    0.762   0.148        0.521            1
##    48      4       2    0.508   0.177        0.257            1
##    60      4       0    0.508   0.177        0.257            1
## 
##                 RX_SUMM_RADIATION_F=Beam + Imp or Isotopes 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12      7       2    0.778   0.139        0.549        1.000
##    24      5       2    0.556   0.166        0.310        0.997
##    36      3       2    0.333   0.157        0.132        0.840
##    48      3       0    0.333   0.157        0.132        0.840
##    60      2       0    0.333   0.157        0.132        0.840
## 
##                 RX_SUMM_RADIATION_F=Radiation, NOS 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     69      39    0.647  0.0456        0.564        0.743
##    24     50      14    0.513  0.0482        0.427        0.617
##    36     32      12    0.384  0.0485        0.300        0.491
##    48     25       4    0.332  0.0483        0.250        0.442
##    60     20       4    0.278  0.0474        0.199        0.389
##   120      6       3    0.208  0.0521        0.127        0.340
## 
##                 RX_SUMM_RADIATION_F=Unknown 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1904     115    0.945 0.00501        0.935        0.955
##    24   1721      85    0.902 0.00662        0.889        0.915
##    36   1571      64    0.867 0.00764        0.852        0.882
##    48   1461      49    0.839 0.00835        0.823        0.856
##    60   1335      56    0.807 0.00911        0.789        0.825
##   120    442     198    0.639 0.01323        0.613        0.665
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  RX_SUMM_RADIATION_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_SUMM_RADIATION_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                               coef exp(coef) se(coef)
## RX_SUMM_RADIATION_FBeam Radiation          1.83534   6.26725  0.01167
## RX_SUMM_RADIATION_FRadioactive Implants    0.98420   2.67568  0.22364
## RX_SUMM_RADIATION_FRadioisotopes           0.88426   2.42118  0.44723
## RX_SUMM_RADIATION_FBeam + Imp or Isotopes  1.38614   3.99938  0.40826
## RX_SUMM_RADIATION_FRadiation, NOS          1.60332   4.96951  0.11257
## RX_SUMM_RADIATION_FUnknown                -0.08043   0.92272  0.04105
##                                                 z Pr(>|z|)    
## RX_SUMM_RADIATION_FBeam Radiation         157.210  < 2e-16 ***
## RX_SUMM_RADIATION_FRadioactive Implants     4.401 1.08e-05 ***
## RX_SUMM_RADIATION_FRadioisotopes            1.977 0.048020 *  
## RX_SUMM_RADIATION_FBeam + Imp or Isotopes   3.395 0.000686 ***
## RX_SUMM_RADIATION_FRadiation, NOS          14.243  < 2e-16 ***
## RX_SUMM_RADIATION_FUnknown                 -1.959 0.050089 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                           exp(coef) exp(-coef) lower .95
## RX_SUMM_RADIATION_FBeam Radiation            6.2673     0.1596    6.1255
## RX_SUMM_RADIATION_FRadioactive Implants      2.6757     0.3737    1.7261
## RX_SUMM_RADIATION_FRadioisotopes             2.4212     0.4130    1.0077
## RX_SUMM_RADIATION_FBeam + Imp or Isotopes    3.9994     0.2500    1.7967
## RX_SUMM_RADIATION_FRadiation, NOS            4.9695     0.2012    3.9856
## RX_SUMM_RADIATION_FUnknown                   0.9227     1.0838    0.8514
##                                           upper .95
## RX_SUMM_RADIATION_FBeam Radiation             6.412
## RX_SUMM_RADIATION_FRadioactive Implants       4.148
## RX_SUMM_RADIATION_FRadioisotopes              5.817
## RX_SUMM_RADIATION_FBeam + Imp or Isotopes     8.902
## RX_SUMM_RADIATION_FRadiation, NOS             6.196
## RX_SUMM_RADIATION_FUnknown                    1.000
## 
## Concordance= 0.552  (se = 0 )
## Rsquare= 0.043   (max possible= 0.998 )
## Likelihood ratio test= 15804  on 6 df,   p=0
## Wald test            = 24931  on 6 df,   p=0
## Score (logrank) test = 32711  on 6 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_SUMM_RADIATION_F

Lymphovascular Invasion

uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)

## _________________________________________________
##    
## ## LYMPH_VASCULAR_INVASION_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
## 
##    159900 observations deleted due to missingness 
##                                                  n events median 0.95LCL
## LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 145065  21475  95.93   95.05
## LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv   7694   3337  48.66   46.06
## LYMPH_VASCULAR_INVASION_F=N_A                   50     30   8.87    6.87
## LYMPH_VASCULAR_INVASION_F=Unknown            50201  13034  94.62   94.03
##                                             0.95UCL
## LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv      NA
## LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv    51.9
## LYMPH_VASCULAR_INVASION_F=N_A                    NA
## LYMPH_VASCULAR_INVASION_F=Unknown                NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
## 
## 159900 observations deleted due to missingness 
##                 LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 120432    4744    0.964 0.000508        0.963        0.965
##    24  97371    5627    0.916 0.000793        0.915        0.918
##    36  70251    4462    0.868 0.001026        0.866        0.870
##    48  47908    3006    0.825 0.001246        0.822        0.827
##    60  29223    1942    0.784 0.001498        0.781        0.787
## 
##                 LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5771    1247    0.830 0.00440        0.821        0.838
##    24   4077    1019    0.674 0.00567        0.663        0.686
##    36   2690     553    0.573 0.00626        0.561        0.585
##    48   1720     284    0.503 0.00675        0.490        0.516
##    60    969     134    0.455 0.00730        0.441        0.469
## 
##                 LYMPH_VASCULAR_INVASION_F=N_A 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     18      25    0.471  0.0734       0.3470        0.639
##    24      7       3    0.369  0.0783       0.2434        0.559
##    36      1       2    0.197  0.1007       0.0722        0.536
## 
##                 LYMPH_VASCULAR_INVASION_F=Unknown 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  37970    6232    0.869 0.00155        0.866        0.872
##    24  30826    2671    0.804 0.00188        0.800        0.808
##    36  23552    1715    0.755 0.00211        0.750        0.759
##    48  17056    1098    0.715 0.00232        0.710        0.719
##    60  10962     668    0.681 0.00255        0.676        0.686
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  LYMPH_VASCULAR_INVASION_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ LYMPH_VASCULAR_INVASION_F, data = data)
## 
##   n= 203010, number of events= 37876 
##    (159900 observations deleted due to missingness)
## 
##                                                coef exp(coef) se(coef)
## LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv  1.26944   3.55885  0.01862
## LYMPH_VASCULAR_INVASION_FN_A                2.54386  12.72866  0.18275
## LYMPH_VASCULAR_INVASION_FUnknown            0.60603   1.83314  0.01111
##                                                z Pr(>|z|)    
## LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv 68.17   <2e-16 ***
## LYMPH_VASCULAR_INVASION_FN_A               13.92   <2e-16 ***
## LYMPH_VASCULAR_INVASION_FUnknown           54.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                            exp(coef) exp(-coef) lower .95
## LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv     3.559    0.28099     3.431
## LYMPH_VASCULAR_INVASION_FN_A                  12.729    0.07856     8.897
## LYMPH_VASCULAR_INVASION_FUnknown               1.833    0.54551     1.794
##                                            upper .95
## LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv     3.691
## LYMPH_VASCULAR_INVASION_FN_A                  18.211
## LYMPH_VASCULAR_INVASION_FUnknown               1.873
## 
## Concordance= 0.609  (se = 0.001 )
## Rsquare= 0.027   (max possible= 0.987 )
## Likelihood ratio test= 5553  on 3 df,   p=0
## Wald test            = 6417  on 3 df,   p=0
## Score (logrank) test = 7039  on 3 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  LYMPH_VASCULAR_INVASION_F

Endoscopic/Robotic

uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)

## _________________________________________________
##    
## ## RX_HOSP_SURG_APPR_2010_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
## 
##    159900 observations deleted due to missingness 
##                                                n events median 0.95LCL
## RX_HOSP_SURG_APPR_2010_F=No_Surg           18689   8965   36.0   33.97
## RX_HOSP_SURG_APPR_2010_F=Robot_Assist        148     26     NA      NA
## RX_HOSP_SURG_APPR_2010_F=Robot_to_Open        37      5     NA   56.71
## RX_HOSP_SURG_APPR_2010_F=Endo_Lap            441    109     NA   85.29
## RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open    170     31     NA   81.51
## RX_HOSP_SURG_APPR_2010_F=Open_Unknown     183492  28727   95.5   95.05
## RX_HOSP_SURG_APPR_2010_F=Unknown              33     13   14.8    6.24
##                                           0.95UCL
## RX_HOSP_SURG_APPR_2010_F=No_Surg             37.9
## RX_HOSP_SURG_APPR_2010_F=Robot_Assist          NA
## RX_HOSP_SURG_APPR_2010_F=Robot_to_Open         NA
## RX_HOSP_SURG_APPR_2010_F=Endo_Lap              NA
## RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open      NA
## RX_HOSP_SURG_APPR_2010_F=Open_Unknown          NA
## RX_HOSP_SURG_APPR_2010_F=Unknown               NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
## 
## 159900 observations deleted due to missingness 
##                 RX_HOSP_SURG_APPR_2010_F=No_Surg 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12  10832    5927    0.665 0.00356        0.659        0.672
##    24   7778    1575    0.562 0.00385        0.555        0.570
##    36   5216     764    0.500 0.00404        0.492        0.508
##    48   3429     385    0.457 0.00425        0.449        0.465
##    60   2057     170    0.429 0.00452        0.420        0.438
## 
##                 RX_HOSP_SURG_APPR_2010_F=Robot_Assist 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    121       8    0.941  0.0204        0.902        0.981
##    24     99       6    0.890  0.0280        0.836        0.946
##    36     74       6    0.828  0.0357        0.761        0.901
##    48     48       3    0.791  0.0400        0.716        0.873
##    60     24       1    0.766  0.0458        0.681        0.861
## 
##                 RX_HOSP_SURG_APPR_2010_F=Robot_to_Open 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     28       3    0.908  0.0508        0.814            1
##    24     21       0    0.908  0.0508        0.814            1
##    36     17       0    0.908  0.0508        0.814            1
##    48     12       0    0.908  0.0508        0.814            1
##    60      2       2    0.692  0.1398        0.465            1
## 
##                 RX_HOSP_SURG_APPR_2010_F=Endo_Lap 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    342      38    0.906  0.0145        0.878        0.935
##    24    269      24    0.837  0.0190        0.801        0.876
##    36    189      24    0.753  0.0237        0.708        0.801
##    48    122      14    0.687  0.0275        0.635        0.743
##    60     66       8    0.636  0.0308        0.579        0.700
## 
##                 RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    134       7    0.954  0.0169        0.922        0.988
##    24    119       4    0.924  0.0221        0.882        0.968
##    36    101       6    0.874  0.0289        0.819        0.932
##    48     73       3    0.845  0.0324        0.784        0.911
##    60     54       1    0.833  0.0341        0.769        0.903
## 
##                 RX_HOSP_SURG_APPR_2010_F=Open_Unknown 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 152722    6256    0.963 0.000460        0.962        0.964
##    24 123988    7707    0.911 0.000723        0.910        0.912
##    36  90891    5932    0.862 0.000925        0.860        0.864
##    48  62997    3983    0.818 0.001110        0.816        0.820
##    60  38949    2562    0.777 0.001317        0.775        0.780
## 
##                 RX_HOSP_SURG_APPR_2010_F=Unknown 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     12       9    0.601   0.105        0.427        0.846
##    24      7       4    0.401   0.108        0.237        0.679
##    36      6       0    0.401   0.108        0.237        0.679
##    48      3       0    0.401   0.108        0.237        0.679
##    60      2       0    0.401   0.108        0.237        0.679
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  RX_HOSP_SURG_APPR_2010_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
## 
##   n= 203010, number of events= 37876 
##    (159900 observations deleted due to missingness)
## 
##                                              coef exp(coef) se(coef)
## RX_HOSP_SURG_APPR_2010_FRobot_Assist     -1.40546   0.24526  0.19640
## RX_HOSP_SURG_APPR_2010_FRobot_to_Open    -1.52765   0.21705  0.44734
## RX_HOSP_SURG_APPR_2010_FEndo_Lap         -0.96163   0.38227  0.09637
## RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open -1.49545   0.22415  0.17993
## RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -1.53853   0.21470  0.01213
## RX_HOSP_SURG_APPR_2010_FUnknown           0.33426   1.39691  0.27755
##                                                 z Pr(>|z|)    
## RX_HOSP_SURG_APPR_2010_FRobot_Assist       -7.156 8.31e-13 ***
## RX_HOSP_SURG_APPR_2010_FRobot_to_Open      -3.415 0.000638 ***
## RX_HOSP_SURG_APPR_2010_FEndo_Lap           -9.979  < 2e-16 ***
## RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open   -8.312  < 2e-16 ***
## RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -126.816  < 2e-16 ***
## RX_HOSP_SURG_APPR_2010_FUnknown             1.204 0.228472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                          exp(coef) exp(-coef) lower .95
## RX_HOSP_SURG_APPR_2010_FRobot_Assist        0.2453     4.0774   0.16689
## RX_HOSP_SURG_APPR_2010_FRobot_to_Open       0.2170     4.6073   0.09032
## RX_HOSP_SURG_APPR_2010_FEndo_Lap            0.3823     2.6160   0.31648
## RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open    0.2241     4.4614   0.15754
## RX_HOSP_SURG_APPR_2010_FOpen_Unknown        0.2147     4.6577   0.20965
## RX_HOSP_SURG_APPR_2010_FUnknown             1.3969     0.7159   0.81080
##                                          upper .95
## RX_HOSP_SURG_APPR_2010_FRobot_Assist        0.3604
## RX_HOSP_SURG_APPR_2010_FRobot_to_Open       0.5216
## RX_HOSP_SURG_APPR_2010_FEndo_Lap            0.4617
## RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open    0.3189
## RX_HOSP_SURG_APPR_2010_FOpen_Unknown        0.2199
## RX_HOSP_SURG_APPR_2010_FUnknown             2.4067
## 
## Concordance= 0.617  (se = 0.001 )
## Rsquare= 0.057   (max possible= 0.987 )
## Likelihood ratio test= 11999  on 6 df,   p=0
## Wald test            = 16121  on 6 df,   p=0
## Score (logrank) test = 19537  on 6 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_HOSP_SURG_APPR_2010_F

Surgery Radiation Sequence

uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)

## _________________________________________________
##    
## ## SURG_RAD_SEQ
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURG_RAD_SEQ, data = data)
## 
##                                             n events median 0.95LCL
## SURG_RAD_SEQ=Surg Alone                337579  73170     NA  164.60
## SURG_RAD_SEQ=Surg then Rad               6249   3820  32.16   30.78
## SURG_RAD_SEQ=Rad Alone                   5240   4377   6.14    5.88
## SURG_RAD_SEQ=No Treatment               10837   7403  11.43   10.97
## SURG_RAD_SEQ=Other                       2891   1115 132.83  120.51
## SURG_RAD_SEQ=Rad before and after Surg     17     14   9.07    4.01
## SURG_RAD_SEQ=Rad then Surg                 97     73  15.21   10.87
##                                        0.95UCL
## SURG_RAD_SEQ=Surg Alone                     NA
## SURG_RAD_SEQ=Surg then Rad               33.77
## SURG_RAD_SEQ=Rad Alone                    6.51
## SURG_RAD_SEQ=No Treatment                12.00
## SURG_RAD_SEQ=Other                      150.70
## SURG_RAD_SEQ=Rad before and after Surg   24.11
## SURG_RAD_SEQ=Rad then Surg               23.49
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURG_RAD_SEQ, data = data)
## 
##                 SURG_RAD_SEQ=Surg Alone 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 291152   11156    0.965 0.000328        0.964        0.965
##    24 251965   14780    0.914 0.000514        0.913        0.915
##    36 209336   12350    0.866 0.000642        0.865        0.867
##    48 172875    9282    0.825 0.000741        0.823        0.826
##    60 140127    7012    0.788 0.000826        0.787        0.790
##   120  33580   16468    0.642 0.001299        0.640        0.645
## 
##                 SURG_RAD_SEQ=Surg then Rad 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4522    1515    0.753 0.00550        0.743        0.764
##    24   3177     999    0.581 0.00642        0.568        0.593
##    36   2276     561    0.472 0.00666        0.459        0.485
##    48   1677     287    0.408 0.00675        0.395        0.421
##    60   1238     174    0.362 0.00684        0.348        0.375
##   120    209     262    0.237 0.00844        0.221        0.254
## 
##                 SURG_RAD_SEQ=Rad Alone 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1594    3476   0.3258 0.00657       0.3132        0.339
##    24    886     551   0.2074 0.00582       0.1963        0.219
##    36    552     180   0.1609 0.00546       0.1505        0.172
##    48    392      76   0.1369 0.00530       0.1269        0.148
##    60    280      38   0.1225 0.00524       0.1126        0.133
##   120     45      48   0.0874 0.00613       0.0762        0.100
## 
##                 SURG_RAD_SEQ=No Treatment 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   4680    5246    0.490 0.00496        0.480        0.500
##    24   3105    1132    0.366 0.00490        0.356        0.376
##    36   2195     459    0.308 0.00482        0.298        0.317
##    48   1608     241    0.271 0.00480        0.261        0.280
##    60   1225     115    0.249 0.00482        0.240        0.259
##   120    272     191    0.191 0.00542        0.181        0.202
## 
##                 SURG_RAD_SEQ=Other 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   2148     502    0.818 0.00736        0.804        0.832
##    24   1888     141    0.763 0.00820        0.747        0.779
##    36   1694      93    0.724 0.00871        0.707        0.741
##    48   1559      63    0.697 0.00904        0.679        0.715
##    60   1413      70    0.665 0.00940        0.646        0.683
##   120    459     214    0.524 0.01160        0.502        0.547
## 
##                 SURG_RAD_SEQ=Rad before and after Surg 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12      6      11    0.353  0.1159       0.1854        0.672
##    24      4       2    0.235  0.1029       0.0999        0.554
##    36      3       1    0.176  0.0925       0.0632        0.493
##    48      3       0    0.176  0.0925       0.0632        0.493
##    60      3       0    0.176  0.0925       0.0632        0.493
##   120      3       0    0.176  0.0925       0.0632        0.493
## 
##                 SURG_RAD_SEQ=Rad then Surg 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12     55      41    0.571  0.0507       0.4798        0.680
##    24     34      18    0.378  0.0500       0.2914        0.490
##    36     23       7    0.295  0.0480       0.2148        0.406
##    48     20       0    0.295  0.0480       0.2148        0.406
##    60     16       1    0.279  0.0480       0.1991        0.391
##   120      4       6    0.109  0.0508       0.0439        0.272
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  SURG_RAD_SEQ
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURG_RAD_SEQ, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                                           coef exp(coef) se(coef)       z
## SURG_RAD_SEQSurg then Rad              1.46805   4.34076  0.01663  88.286
## SURG_RAD_SEQRad Alone                  2.66145  14.31699  0.01575 168.937
## SURG_RAD_SEQNo Treatment               2.06172   7.85949  0.01226 168.104
## SURG_RAD_SEQOther                      0.54046   1.71679  0.03018  17.905
## SURG_RAD_SEQRad before and after Surg  2.01488   7.49982  0.26729   7.538
## SURG_RAD_SEQRad then Surg              1.93211   6.90408  0.11711  16.499
##                                       Pr(>|z|)    
## SURG_RAD_SEQSurg then Rad              < 2e-16 ***
## SURG_RAD_SEQRad Alone                  < 2e-16 ***
## SURG_RAD_SEQNo Treatment               < 2e-16 ***
## SURG_RAD_SEQOther                      < 2e-16 ***
## SURG_RAD_SEQRad before and after Surg 4.77e-14 ***
## SURG_RAD_SEQRad then Surg              < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                       exp(coef) exp(-coef) lower .95
## SURG_RAD_SEQSurg then Rad                 4.341    0.23037     4.202
## SURG_RAD_SEQRad Alone                    14.317    0.06985    13.882
## SURG_RAD_SEQNo Treatment                  7.859    0.12723     7.673
## SURG_RAD_SEQOther                         1.717    0.58248     1.618
## SURG_RAD_SEQRad before and after Surg     7.500    0.13334     4.441
## SURG_RAD_SEQRad then Surg                 6.904    0.14484     5.488
##                                       upper .95
## SURG_RAD_SEQSurg then Rad                 4.485
## SURG_RAD_SEQRad Alone                    14.766
## SURG_RAD_SEQNo Treatment                  8.051
## SURG_RAD_SEQOther                         1.821
## SURG_RAD_SEQRad before and after Surg    12.664
## SURG_RAD_SEQRad then Surg                 8.685
## 
## Concordance= 0.605  (se = 0 )
## Rsquare= 0.094   (max possible= 0.998 )
## Likelihood ratio test= 35691  on 6 df,   p=0
## Wald test            = 57107  on 6 df,   p=0
## Score (logrank) test = 86266  on 6 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  SURG_RAD_SEQ

Surgery Yes/No

uni_var(test_var = "SURGERY_YN", data_imp = data)

## _________________________________________________
##    
## ## SURGERY_YN
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURGERY_YN, data = data)
## 
##                     n events median 0.95LCL 0.95UCL
## SURGERY_YN=No   16214  11864   8.77    8.48     9.1
## SURGERY_YN=Ukn    684    462  14.19   11.83    16.4
## SURGERY_YN=Yes 346012  77646 165.39  164.57      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURGERY_YN, data = data)
## 
##                 SURGERY_YN=No 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6305    8805    0.434 0.00400        0.426        0.442
##    24   4021    1679    0.313 0.00383        0.305        0.320
##    36   2771     641    0.259 0.00372        0.251        0.266
##    48   2025     316    0.226 0.00367        0.219        0.234
##    60   1528     153    0.208 0.00367        0.201        0.215
##   120    323     243    0.157 0.00413        0.149        0.166
## 
##                 SURGERY_YN=Ukn 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12    310     291    0.533  0.0201        0.495        0.574
##    24    205      79    0.392  0.0201        0.355        0.433
##    36    148      37    0.316  0.0197        0.280        0.357
##    48    120      15    0.282  0.0195        0.247        0.323
##    60     93      18    0.238  0.0190        0.204        0.279
##   120     22      21    0.172  0.0186        0.139        0.212
## 
##                 SURGERY_YN=Yes 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 297542   12851    0.960 0.000342        0.960        0.961
##    24 256833   15865    0.907 0.000524        0.906        0.908
##    36 213160   12973    0.858 0.000648        0.857        0.859
##    48 175989    9618    0.817 0.000743        0.815        0.818
##    60 142681    7239    0.780 0.000824        0.779        0.782
##   120  34227   16925    0.635 0.001283        0.632        0.637
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  SURGERY_YN
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURGERY_YN, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                   coef exp(coef) se(coef)        z Pr(>|z|)    
## SURGERY_YNUkn -0.27473   0.75977  0.04742   -5.793 6.91e-09 ***
## SURGERY_YNYes -2.19889   0.11093  0.00998 -220.333  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## SURGERY_YNUkn    0.7598      1.316    0.6923    0.8338
## SURGERY_YNYes    0.1109      9.015    0.1088    0.1131
## 
## Concordance= 0.585  (se = 0 )
## Rsquare= 0.08   (max possible= 0.998 )
## Likelihood ratio test= 30337  on 2 df,   p=0
## Wald test            = 49726  on 2 df,   p=0
## Score (logrank) test = 72791  on 2 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  SURGERY_YN

Radiation Yes/No

uni_var(test_var = "RADIATION_YN", data_imp = data)

## _________________________________________________
##    
## ## RADIATION_YN
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RADIATION_YN, data = data)
## 
##    2370 observations deleted due to missingness 
##                       n events median 0.95LCL 0.95UCL
## RADIATION_YN=No  348906  80900  164.7   164.5      NA
## RADIATION_YN=Yes  11634   8300   15.7    15.1    16.4
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RADIATION_YN, data = data)
## 
## 2370 observations deleted due to missingness 
##                 RADIATION_YN=No 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 296056   16607    0.950 0.000381        0.949        0.950
##    24 255222   15967    0.896 0.000546        0.895        0.897
##    36 211643   12837    0.848 0.000662        0.847        0.849
##    48 174573    9535    0.807 0.000752        0.806        0.809
##    60 141422    7141    0.771 0.000829        0.770        0.773
##   120  33869   16671    0.628 0.001277        0.626        0.631
## 
##                 RADIATION_YN=Yes 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   6194    5053    0.559 0.00465        0.550        0.569
##    24   4114    1571    0.412 0.00469        0.402        0.421
##    36   2864     749    0.331 0.00460        0.323        0.341
##    48   2099     365    0.286 0.00455        0.277        0.295
##    60   1544     213    0.254 0.00454        0.246        0.263
##   120    261     319    0.168 0.00540        0.158        0.179
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  RADIATION_YN
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ RADIATION_YN, data = data)
## 
##   n= 360540, number of events= 89200 
##    (2370 observations deleted due to missingness)
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)    
## RADIATION_YNYes 1.83188   6.24562  0.01161 157.9   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## RADIATION_YNYes     6.246     0.1601     6.105     6.389
## 
## Concordance= 0.552  (se = 0 )
## Rsquare= 0.043   (max possible= 0.998 )
## Likelihood ratio test= 15806  on 1 df,   p=0
## Wald test            = 24917  on 1 df,   p=0
## Score (logrank) test = 32677  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  RADIATION_YN

Chemo Yes/No

uni_var(test_var = "CHEMO_YN", data_imp = data)

## _________________________________________________
##    
## ## CHEMO_YN
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CHEMO_YN, data = data)
## 
##                   n events median 0.95LCL 0.95UCL
## CHEMO_YN=No  342669  80729  164.6   164.4      NA
## CHEMO_YN=Yes   9216   6400   16.6    15.8    17.2
## CHEMO_YN=Ukn  11025   2843     NA   160.7      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CHEMO_YN, data = data)
## 
##                 CHEMO_YN=No 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 289704   17488    0.946 0.000396        0.945        0.947
##    24 249482   15703    0.893 0.000559        0.892        0.894
##    36 206545   12671    0.844 0.000674        0.843        0.846
##    48 170118    9390    0.803 0.000764        0.802        0.805
##    60 137619    7033    0.767 0.000842        0.765        0.769
##   120  32671   16359    0.623 0.001295        0.621        0.626
## 
##                 CHEMO_YN=Yes 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   5063    3783    0.581 0.00521        0.571        0.591
##    24   3350    1399    0.415 0.00529        0.405        0.426
##    36   2470     559    0.342 0.00518        0.332        0.353
##    48   1923     272    0.303 0.00511        0.293        0.313
##    60   1520     133    0.280 0.00509        0.270        0.290
##   120    371     228    0.220 0.00551        0.210        0.231
## 
##                 CHEMO_YN=Ukn 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   9390     676    0.936 0.00239        0.931        0.940
##    24   8227     521    0.882 0.00321        0.876        0.888
##    36   7064     421    0.835 0.00378        0.827        0.842
##    48   6093     287    0.799 0.00417        0.791        0.807
##    60   5163     244    0.765 0.00452        0.756        0.774
##   120   1530     602    0.635 0.00635        0.622        0.647
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  CHEMO_YN
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ CHEMO_YN, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                  coef exp(coef)  se(coef)       z Pr(>|z|)    
## CHEMO_YNYes  1.670322  5.313878  0.013030 128.186   <2e-16 ***
## CHEMO_YNUkn -0.003705  0.996302  0.019086  -0.194    0.846    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##             exp(coef) exp(-coef) lower .95 upper .95
## CHEMO_YNYes    5.3139     0.1882    5.1799     5.451
## CHEMO_YNUkn    0.9963     1.0037    0.9597     1.034
## 
## Concordance= 0.538  (se = 0 )
## Rsquare= 0.029   (max possible= 0.998 )
## Likelihood ratio test= 10647  on 2 df,   p=0
## Wald test            = 16474  on 2 df,   p=0
## Score (logrank) test = 20670  on 2 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  CHEMO_YN

Treatment Yes/No

uni_var(test_var = "Tx_YN", data_imp = data)

## _________________________________________________
##    
## ## Tx_YN
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ Tx_YN, data = data)
## 
##    11025 observations deleted due to missingness 
##                  n events median 0.95LCL 0.95UCL
## Tx_YN=FALSE   8215   5411     12    11.2    12.8
## Tx_YN=TRUE  343670  81718    165   164.4      NA
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ Tx_YN, data = data)
## 
## 11025 observations deleted due to missingness 
##                 Tx_YN=FALSE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   3583    3896    0.500 0.00571        0.489        0.511
##    24   2501     723    0.394 0.00570        0.383        0.405
##    36   1796     340    0.336 0.00567        0.325        0.347
##    48   1323     182    0.299 0.00568        0.288        0.310
##    60   1017      90    0.276 0.00573        0.265        0.288
##   120    212     166    0.208 0.00660        0.195        0.221
## 
##                 Tx_YN=TRUE 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 291184   17375    0.946 0.000396        0.946        0.947
##    24 250331   16379    0.891 0.000562        0.890        0.892
##    36 207219   12890    0.842 0.000677        0.841        0.843
##    48 170718    9480    0.801 0.000766        0.799        0.802
##    60 138122    7076    0.765 0.000843        0.763        0.766
##   120  32830   16421    0.621 0.001290        0.619        0.624
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  Tx_YN
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ Tx_YN, data = data)
## 
##   n= 351885, number of events= 87129 
##    (11025 observations deleted due to missingness)
## 
##               coef exp(coef) se(coef)      z Pr(>|z|)    
## Tx_YNTRUE -1.86336   0.15515  0.01408 -132.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##           exp(coef) exp(-coef) lower .95 upper .95
## Tx_YNTRUE    0.1552      6.445    0.1509    0.1595
## 
## Concordance= 0.537  (se = 0 )
## Rsquare= 0.03   (max possible= 0.998 )
## Likelihood ratio test= 10747  on 1 df,   p=0
## Wald test            = 17504  on 1 df,   p=0
## Score (logrank) test = 23183  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  Tx_YN

Metastases at Dx

uni_var(test_var = "mets_at_dx_F", data_imp = data)

## _________________________________________________
##    
## ## mets_at_dx_F
## _________________________________________________
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ mets_at_dx_F, data = data)
## 
##                         n events median 0.95LCL 0.95UCL
## mets_at_dx_F=FALSE 357195  85269 164.57  162.89      NA
## mets_at_dx_F=TRUE    5715   4703   5.91    5.62    6.14
## 
## Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ mets_at_dx_F, data = data)
## 
##                 mets_at_dx_F=FALSE 
##  time n.risk n.event survival  std.err lower 95% CI upper 95% CI
##    12 302553   18162    0.946 0.000389        0.945        0.947
##    24 260282   17003    0.891 0.000551        0.890        0.892
##    36 215704   13444    0.842 0.000664        0.840        0.843
##    48 177949    9891    0.800 0.000751        0.799        0.802
##    60 144215    7387    0.764 0.000826        0.763        0.766
##   120  34572   17179    0.621 0.001260        0.619        0.624
## 
##                 mets_at_dx_F=TRUE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##    12   1604    3785   0.3137 0.00631       0.3015       0.3263
##    24    777     620   0.1841 0.00547       0.1737       0.1951
##    36    375     207   0.1265 0.00505       0.1170       0.1368
##    48    185      58   0.1030 0.00499       0.0936       0.1132
##    60     87      23   0.0863 0.00530       0.0765       0.0974
## 
## 
## 
## 
##    
## ## Univariable Cox Proportional Hazard Model for:  mets_at_dx_F
## 
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ mets_at_dx_F, data = data)
## 
##   n= 362910, number of events= 89972 
## 
##                      coef exp(coef) se(coef)   z Pr(>|z|)    
## mets_at_dx_FTRUE  2.76699  15.91061  0.01537 180   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## mets_at_dx_FTRUE     15.91    0.06285     15.44      16.4
## 
## Concordance= 0.535  (se = 0 )
## Rsquare= 0.045   (max possible= 0.998 )
## Likelihood ratio test= 16759  on 1 df,   p=0
## Wald test            = 32390  on 1 df,   p=0
## Score (logrank) test = 58926  on 1 df,   p=0
## 
## 
## 
## 
## 
##    
## ## Unadjusted Kaplan Meier Overall Survival Curve for:  mets_at_dx_F

Tumor specific Variables

Node Size

Cox Proportional Hazard Ratio

Model #1

Full analysis

model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
model_one %>% summary()
## Call:
## coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
##     0) ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + 
##     INCOME_F + U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + 
##     EDUCATION_F, data = data)
## 
##   n= 310618, number of events= 84098 
##    (52292 observations deleted due to missingness)
## 
##                                                   coef exp(coef)  se(coef)
## SURG_RAD_SEQSurg then Rad                     1.175093  3.238443  0.017436
## SURG_RAD_SEQRad Alone                         2.488459 12.042700  0.016545
## SURG_RAD_SEQNo Treatment                      1.918921  6.813599  0.012812
## SURG_RAD_SEQOther                             0.527635  1.694918  0.031302
## SURG_RAD_SEQRad before and after Surg         2.664354 14.358675  0.288773
## SURG_RAD_SEQRad then Surg                     1.634362  5.126184  0.126140
## INSURANCE_FNone                               0.816196  2.261881  0.023242
## INSURANCE_FMedicaid                           0.950788  2.587747  0.021840
## INSURANCE_FMedicare                           0.172193  1.187907  0.009972
## INSURANCE_FOther Government                   0.226541  1.254254  0.037211
## INSURANCE_FUnknown                                  NA        NA  0.000000
## AGE                                           0.055292  1.056849  0.000389
## SEX_FFemale                                  -0.317842  0.727718  0.007424
## RACE_FBlack                                   0.402027  1.494852  0.035050
## RACE_FOther/Unk                              -0.089698  0.914207  0.030340
## RACE_FAsian                                   0.228528  1.256749  0.061677
## INCOME_F$38,000 - $47,999                    -0.047120  0.953973  0.012448
## INCOME_F$48,000 - $62,999                    -0.080485  0.922669  0.013199
## INCOME_F$63,000 +                            -0.148064  0.862376  0.014837
## U_R_FUrban                                   -0.035235  0.965379  0.010583
## U_R_FRural                                   -0.011491  0.988575  0.024840
## FACILITY_TYPE_FComprehensive Comm Ca Program -0.046446  0.954616  0.013827
## FACILITY_TYPE_FAcademic/Research Program     -0.137317  0.871694  0.013803
## FACILITY_TYPE_FIntegrated Network Ca Program -0.019513  0.980677  0.016205
## FACILITY_LOCATION_FMiddle Atlantic           -0.004003  0.996005  0.016806
## FACILITY_LOCATION_FSouth Atlantic            -0.027797  0.972586  0.016273
## FACILITY_LOCATION_FEast North Central         0.073783  1.076573  0.016696
## FACILITY_LOCATION_FEast South Central         0.062075  1.064043  0.019632
## FACILITY_LOCATION_FWest North Central         0.033008  1.033559  0.019111
## FACILITY_LOCATION_FWest South Central         0.015189  1.015304  0.020821
## FACILITY_LOCATION_FMountain                   0.021827  1.022067  0.021098
## FACILITY_LOCATION_FPacific                   -0.053757  0.947662  0.017688
## EDUCATION_F13 - 20.9%                        -0.067335  0.934882  0.012642
## EDUCATION_F7 - 12.9%                         -0.117863  0.888818  0.013357
## EDUCATION_FLess than 7%                      -0.255588  0.774461  0.015259
##                                                    z Pr(>|z|)    
## SURG_RAD_SEQSurg then Rad                     67.395  < 2e-16 ***
## SURG_RAD_SEQRad Alone                        150.404  < 2e-16 ***
## SURG_RAD_SEQNo Treatment                     149.775  < 2e-16 ***
## SURG_RAD_SEQOther                             16.856  < 2e-16 ***
## SURG_RAD_SEQRad before and after Surg          9.226  < 2e-16 ***
## SURG_RAD_SEQRad then Surg                     12.957  < 2e-16 ***
## INSURANCE_FNone                               35.117  < 2e-16 ***
## INSURANCE_FMedicaid                           43.535  < 2e-16 ***
## INSURANCE_FMedicare                           17.268  < 2e-16 ***
## INSURANCE_FOther Government                    6.088 1.14e-09 ***
## INSURANCE_FUnknown                                NA       NA    
## AGE                                          142.146  < 2e-16 ***
## SEX_FFemale                                  -42.811  < 2e-16 ***
## RACE_FBlack                                   11.470  < 2e-16 ***
## RACE_FOther/Unk                               -2.956 0.003112 ** 
## RACE_FAsian                                    3.705 0.000211 ***
## INCOME_F$38,000 - $47,999                     -3.785 0.000153 ***
## INCOME_F$48,000 - $62,999                     -6.098 1.08e-09 ***
## INCOME_F$63,000 +                             -9.979  < 2e-16 ***
## U_R_FUrban                                    -3.329 0.000871 ***
## U_R_FRural                                    -0.463 0.643647    
## FACILITY_TYPE_FComprehensive Comm Ca Program  -3.359 0.000782 ***
## FACILITY_TYPE_FAcademic/Research Program      -9.949  < 2e-16 ***
## FACILITY_TYPE_FIntegrated Network Ca Program  -1.204 0.228544    
## FACILITY_LOCATION_FMiddle Atlantic            -0.238 0.811730    
## FACILITY_LOCATION_FSouth Atlantic             -1.708 0.087608 .  
## FACILITY_LOCATION_FEast North Central          4.419 9.90e-06 ***
## FACILITY_LOCATION_FEast South Central          3.162 0.001567 ** 
## FACILITY_LOCATION_FWest North Central          1.727 0.084140 .  
## FACILITY_LOCATION_FWest South Central          0.729 0.465706    
## FACILITY_LOCATION_FMountain                    1.035 0.300893    
## FACILITY_LOCATION_FPacific                    -3.039 0.002372 ** 
## EDUCATION_F13 - 20.9%                         -5.326 1.00e-07 ***
## EDUCATION_F7 - 12.9%                          -8.824  < 2e-16 ***
## EDUCATION_FLess than 7%                      -16.750  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                                              exp(coef) exp(-coef)
## SURG_RAD_SEQSurg then Rad                       3.2384    0.30879
## SURG_RAD_SEQRad Alone                          12.0427    0.08304
## SURG_RAD_SEQNo Treatment                        6.8136    0.14677
## SURG_RAD_SEQOther                               1.6949    0.59000
## SURG_RAD_SEQRad before and after Surg          14.3587    0.06964
## SURG_RAD_SEQRad then Surg                       5.1262    0.19508
## INSURANCE_FNone                                 2.2619    0.44211
## INSURANCE_FMedicaid                             2.5877    0.38644
## INSURANCE_FMedicare                             1.1879    0.84182
## INSURANCE_FOther Government                     1.2543    0.79729
## INSURANCE_FUnknown                                  NA         NA
## AGE                                             1.0568    0.94621
## SEX_FFemale                                     0.7277    1.37416
## RACE_FBlack                                     1.4949    0.66896
## RACE_FOther/Unk                                 0.9142    1.09384
## RACE_FAsian                                     1.2567    0.79570
## INCOME_F$38,000 - $47,999                       0.9540    1.04825
## INCOME_F$48,000 - $62,999                       0.9227    1.08381
## INCOME_F$63,000 +                               0.8624    1.15959
## U_R_FUrban                                      0.9654    1.03586
## U_R_FRural                                      0.9886    1.01156
## FACILITY_TYPE_FComprehensive Comm Ca Program    0.9546    1.04754
## FACILITY_TYPE_FAcademic/Research Program        0.8717    1.14719
## FACILITY_TYPE_FIntegrated Network Ca Program    0.9807    1.01970
## FACILITY_LOCATION_FMiddle Atlantic              0.9960    1.00401
## FACILITY_LOCATION_FSouth Atlantic               0.9726    1.02819
## FACILITY_LOCATION_FEast North Central           1.0766    0.92887
## FACILITY_LOCATION_FEast South Central           1.0640    0.93981
## FACILITY_LOCATION_FWest North Central           1.0336    0.96753
## FACILITY_LOCATION_FWest South Central           1.0153    0.98493
## FACILITY_LOCATION_FMountain                     1.0221    0.97841
## FACILITY_LOCATION_FPacific                      0.9477    1.05523
## EDUCATION_F13 - 20.9%                           0.9349    1.06965
## EDUCATION_F7 - 12.9%                            0.8888    1.12509
## EDUCATION_FLess than 7%                         0.7745    1.29122
##                                              lower .95 upper .95
## SURG_RAD_SEQSurg then Rad                       3.1296    3.3510
## SURG_RAD_SEQRad Alone                          11.6584   12.4396
## SURG_RAD_SEQNo Treatment                        6.6446    6.9869
## SURG_RAD_SEQOther                               1.5941    1.8022
## SURG_RAD_SEQRad before and after Surg           8.1529   25.2882
## SURG_RAD_SEQRad then Surg                       4.0033    6.5639
## INSURANCE_FNone                                 2.1612    2.3673
## INSURANCE_FMedicaid                             2.4793    2.7009
## INSURANCE_FMedicare                             1.1649    1.2114
## INSURANCE_FOther Government                     1.1660    1.3491
## INSURANCE_FUnknown                                  NA        NA
## AGE                                             1.0560    1.0577
## SEX_FFemale                                     0.7172    0.7384
## RACE_FBlack                                     1.3956    1.6012
## RACE_FOther/Unk                                 0.8614    0.9702
## RACE_FAsian                                     1.1136    1.4182
## INCOME_F$38,000 - $47,999                       0.9310    0.9775
## INCOME_F$48,000 - $62,999                       0.8991    0.9468
## INCOME_F$63,000 +                               0.8377    0.8878
## U_R_FUrban                                      0.9456    0.9856
## U_R_FRural                                      0.9416    1.0379
## FACILITY_TYPE_FComprehensive Comm Ca Program    0.9291    0.9808
## FACILITY_TYPE_FAcademic/Research Program        0.8484    0.8956
## FACILITY_TYPE_FIntegrated Network Ca Program    0.9500    1.0123
## FACILITY_LOCATION_FMiddle Atlantic              0.9637    1.0294
## FACILITY_LOCATION_FSouth Atlantic               0.9421    1.0041
## FACILITY_LOCATION_FEast North Central           1.0419    1.1124
## FACILITY_LOCATION_FEast South Central           1.0239    1.1058
## FACILITY_LOCATION_FWest North Central           0.9956    1.0730
## FACILITY_LOCATION_FWest South Central           0.9747    1.0576
## FACILITY_LOCATION_FMountain                     0.9807    1.0652
## FACILITY_LOCATION_FPacific                      0.9154    0.9811
## EDUCATION_F13 - 20.9%                           0.9120    0.9583
## EDUCATION_F7 - 12.9%                            0.8659    0.9124
## EDUCATION_FLess than 7%                         0.7516    0.7980
## 
## Concordance= 0.767  (se = 0.001 )
## Rsquare= 0.226   (max possible= 0.999 )
## Likelihood ratio test= 79560  on 34 df,   p=0
## Wald test            = 91341  on 34 df,   p=0
## Score (logrank) test = 120014  on 34 df,   p=0

Summary of Model

model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))
## # A tibble: 35 x 5
##                                        Variable Hazard_Ratio   conf.low
##                                           <chr>        <dbl>      <dbl>
##  1                    SURG_RAD_SEQSurg then Rad    3.2384432  3.1296434
##  2                        SURG_RAD_SEQRad Alone   12.0426996 11.6584439
##  3                     SURG_RAD_SEQNo Treatment    6.8135992  6.6446328
##  4                            SURG_RAD_SEQOther    1.6949182  1.5940590
##  5        SURG_RAD_SEQRad before and after Surg   14.3586752  8.1528685
##  6                    SURG_RAD_SEQRad then Surg    5.1261839  4.0033497
##  7                              INSURANCE_FNone    2.2618805  2.1611550
##  8                          INSURANCE_FMedicaid    2.5877467  2.4793148
##  9                          INSURANCE_FMedicare    1.1879067  1.1649148
## 10                  INSURANCE_FOther Government    1.2542538  1.1660347
## 11                           INSURANCE_FUnknown           NA         NA
## 12                                          AGE    1.0568495  1.0560440
## 13                                  SEX_FFemale    0.7277177  0.7172049
## 14                                  RACE_FBlack    1.4948519  1.3956069
## 15                              RACE_FOther/Unk    0.9142072  0.8614287
## 16                                  RACE_FAsian    1.2567485  1.1136500
## 17                    INCOME_F$38,000 - $47,999    0.9539725  0.9309795
## 18                    INCOME_F$48,000 - $62,999    0.9226686  0.8991055
## 19                            INCOME_F$63,000 +    0.8623762  0.8376588
## 20                                   U_R_FUrban    0.9653785  0.9455601
## 21                                   U_R_FRural    0.9885746  0.9415980
## 22 FACILITY_TYPE_FComprehensive Comm Ca Program    0.9546160  0.9290925
## 23     FACILITY_TYPE_FAcademic/Research Program    0.8716936  0.8484284
## 24 FACILITY_TYPE_FIntegrated Network Ca Program    0.9806766  0.9500188
## 25           FACILITY_LOCATION_FMiddle Atlantic    0.9960048  0.9637307
## 26            FACILITY_LOCATION_FSouth Atlantic    0.9725862  0.9420556
## 27        FACILITY_LOCATION_FEast North Central    1.0765733  1.0419151
## 28        FACILITY_LOCATION_FEast South Central    1.0640426  1.0238784
## 29        FACILITY_LOCATION_FWest North Central    1.0335592  0.9955606
## 30        FACILITY_LOCATION_FWest South Central    1.0153044  0.9747057
## 31                  FACILITY_LOCATION_FMountain    1.0220666  0.9806641
## 32                   FACILITY_LOCATION_FPacific    0.9476622  0.9153715
## 33                        EDUCATION_F13 - 20.9%    0.9348816  0.9120022
## 34                         EDUCATION_F7 - 12.9%    0.8888182  0.8658518
## 35                      EDUCATION_FLess than 7%    0.7744613  0.7516428
## # ... with 2 more variables: conf.high <dbl>, p.value <dbl>

Predictors of Surgery

fit_surg <- glm(SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = data %>% filter(SURGERY_YN != "Ukn") %>% droplevels() %>% mutate(SURGERY_YN = as.logical(SURGERY_YN)))

summary(fit_surg)
## 
## Call:
## glm(formula = SURG_TF ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F + 
##     FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP, 
##     data = data %>% filter(SURGERY_YN != "Ukn") %>% droplevels() %>% 
##         mutate(SURGERY_YN = as.logical(SURGERY_YN)))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.99423   0.03190   0.04440   0.05726   0.21648  
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                   0.9081399  0.0028620 317.304
## AGE_F(54,64]                                 -0.0072041  0.0010786  -6.679
## AGE_F(64,74]                                 -0.0081639  0.0010934  -7.466
## AGE_F(74,100]                                -0.0200655  0.0010744 -18.675
## SEX_FFemale                                   0.0109075  0.0007849  13.896
## RACE_FBlack                                  -0.0929640  0.0049996 -18.594
## RACE_FOther/Unk                              -0.0006775  0.0030515  -0.222
## RACE_FAsian                                  -0.0518461  0.0074247  -6.983
## INCOME_F$38,000 - $47,999                     0.0041661  0.0015199   2.741
## INCOME_F$48,000 - $62,999                     0.0085346  0.0015873   5.377
## INCOME_F$63,000 +                             0.0110084  0.0017396   6.328
## U_R_FUrban                                    0.0050951  0.0012133   4.199
## U_R_FRural                                    0.0025631  0.0029320   0.874
## FACILITY_TYPE_FComprehensive Comm Ca Program  0.0246966  0.0016691  14.796
## FACILITY_TYPE_FAcademic/Research Program      0.0392540  0.0016438  23.880
## FACILITY_TYPE_FIntegrated Network Ca Program  0.0254413  0.0019155  13.282
## FACILITY_LOCATION_FMiddle Atlantic           -0.0034108  0.0018257  -1.868
## FACILITY_LOCATION_FSouth Atlantic            -0.0059582  0.0018019  -3.307
## FACILITY_LOCATION_FEast North Central        -0.0053532  0.0018364  -2.915
## FACILITY_LOCATION_FEast South Central        -0.0035544  0.0022293  -1.594
## FACILITY_LOCATION_FWest North Central        -0.0093962  0.0020616  -4.558
## FACILITY_LOCATION_FWest South Central        -0.0253217  0.0023654 -10.705
## FACILITY_LOCATION_FMountain                  -0.0120912  0.0023058  -5.244
## FACILITY_LOCATION_FPacific                   -0.0098457  0.0019394  -5.077
## EDUCATION_F13 - 20.9%                         0.0067553  0.0015322   4.409
## EDUCATION_F7 - 12.9%                          0.0114129  0.0015965   7.149
## EDUCATION_FLess than 7%                       0.0162856  0.0017663   9.220
## EXPN_GROUPPre-Expansion                       0.0086333  0.0011013   7.840
##                                              Pr(>|t|)    
## (Intercept)                                   < 2e-16 ***
## AGE_F(54,64]                                 2.41e-11 ***
## AGE_F(64,74]                                 8.27e-14 ***
## AGE_F(74,100]                                 < 2e-16 ***
## SEX_FFemale                                   < 2e-16 ***
## RACE_FBlack                                   < 2e-16 ***
## RACE_FOther/Unk                              0.824293    
## RACE_FAsian                                  2.90e-12 ***
## INCOME_F$38,000 - $47,999                    0.006124 ** 
## INCOME_F$48,000 - $62,999                    7.59e-08 ***
## INCOME_F$63,000 +                            2.49e-10 ***
## U_R_FUrban                                   2.68e-05 ***
## U_R_FRural                                   0.382028    
## FACILITY_TYPE_FComprehensive Comm Ca Program  < 2e-16 ***
## FACILITY_TYPE_FAcademic/Research Program      < 2e-16 ***
## FACILITY_TYPE_FIntegrated Network Ca Program  < 2e-16 ***
## FACILITY_LOCATION_FMiddle Atlantic           0.061737 .  
## FACILITY_LOCATION_FSouth Atlantic            0.000944 ***
## FACILITY_LOCATION_FEast North Central        0.003556 ** 
## FACILITY_LOCATION_FEast South Central        0.110845    
## FACILITY_LOCATION_FWest North Central        5.18e-06 ***
## FACILITY_LOCATION_FWest South Central         < 2e-16 ***
## FACILITY_LOCATION_FMountain                  1.57e-07 ***
## FACILITY_LOCATION_FPacific                   3.84e-07 ***
## EDUCATION_F13 - 20.9%                        1.04e-05 ***
## EDUCATION_F7 - 12.9%                         8.78e-13 ***
## EDUCATION_FLess than 7%                       < 2e-16 ***
## EXPN_GROUPPre-Expansion                      4.54e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04499771)
## 
##     Null deviance: 14065  on 309989  degrees of freedom
## Residual deviance: 13948  on 309962  degrees of freedom
##   (52236 observations deleted due to missingness)
## AIC: -81580
## 
## Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
##                                              Odds ratio     2.5 %
## (Intercept)                                   2.4797057 2.4658347
## AGE_F(54,64]                                  0.9928218 0.9907252
## AGE_F(64,74]                                  0.9918693 0.9897459
## AGE_F(74,100]                                 0.9801345 0.9780726
## SEX_FFemale                                   1.0109672 1.0094131
## RACE_FBlack                                   0.9112263 0.9023407
## RACE_FOther/Unk                               0.9993227 0.9933638
## RACE_FAsian                                   0.9494750 0.9357581
## INCOME_F$38,000 - $47,999                     1.0041748 1.0011879
## INCOME_F$48,000 - $62,999                     1.0085711 1.0054382
## INCOME_F$63,000 +                             1.0110692 1.0076277
## U_R_FUrban                                    1.0051081 1.0027208
## U_R_FRural                                    1.0025663 0.9968215
## FACILITY_TYPE_FComprehensive Comm Ca Program  1.0250041 1.0216563
## FACILITY_TYPE_FAcademic/Research Program      1.0400346 1.0366892
## FACILITY_TYPE_FIntegrated Network Ca Program  1.0257677 1.0219238
## FACILITY_LOCATION_FMiddle Atlantic            0.9965950 0.9930353
## FACILITY_LOCATION_FSouth Atlantic             0.9940596 0.9905551
## FACILITY_LOCATION_FEast North Central         0.9946611 0.9910876
## FACILITY_LOCATION_FEast South Central         0.9964519 0.9921076
## FACILITY_LOCATION_FWest North Central         0.9906478 0.9866529
## FACILITY_LOCATION_FWest South Central         0.9749962 0.9704864
## FACILITY_LOCATION_FMountain                   0.9879816 0.9835266
## FACILITY_LOCATION_FPacific                    0.9902026 0.9864458
## EDUCATION_F13 - 20.9%                         1.0067782 1.0037594
## EDUCATION_F7 - 12.9%                          1.0114782 1.0083182
## EDUCATION_FLess than 7%                       1.0164189 1.0129062
## EXPN_GROUPPre-Expansion                       1.0086707 1.0064959
##                                                 97.5 %
## (Intercept)                                  2.4936548
## AGE_F(54,64]                                 0.9949229
## AGE_F(64,74]                                 0.9939973
## AGE_F(74,100]                                0.9822007
## SEX_FFemale                                  1.0125237
## RACE_FBlack                                  0.9201994
## RACE_FOther/Unk                              1.0053174
## RACE_FAsian                                  0.9633928
## INCOME_F$38,000 - $47,999                    1.0071707
## INCOME_F$48,000 - $62,999                    1.0117137
## INCOME_F$63,000 +                            1.0145225
## U_R_FUrban                                   1.0075010
## U_R_FRural                                   1.0083443
## FACILITY_TYPE_FComprehensive Comm Ca Program 1.0283628
## FACILITY_TYPE_FAcademic/Research Program     1.0433908
## FACILITY_TYPE_FIntegrated Network Ca Program 1.0296261
## FACILITY_LOCATION_FMiddle Atlantic           1.0001676
## FACILITY_LOCATION_FSouth Atlantic            0.9975764
## FACILITY_LOCATION_FEast North Central        0.9982476
## FACILITY_LOCATION_FEast South Central        1.0008152
## FACILITY_LOCATION_FWest North Central        0.9946588
## FACILITY_LOCATION_FWest South Central        0.9795270
## FACILITY_LOCATION_FMountain                  0.9924568
## FACILITY_LOCATION_FPacific                   0.9939737
## EDUCATION_F13 - 20.9%                        1.0098061
## EDUCATION_F7 - 12.9%                         1.0146482
## EDUCATION_FLess than 7%                      1.0199438
## EXPN_GROUPPre-Expansion                      1.0108502

Predictors of Metastasis at Time of Diagnosis, limit to those cases where data

about expansion status is available (> Age 39, non-ambiguous status states)

fit_mets <- glm(mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP,
   data = no_Excludes)

summary(fit_mets)
## 
## Call:
## glm(formula = mets_at_dx_F ~ AGE_F + SEX_F + RACE_F + INCOME_F + 
##     U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + 
##     EXPN_GROUP, data = no_Excludes)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.08216  -0.02878  -0.02092  -0.01372   1.00266  
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                   0.0495593  0.0019342  25.623
## AGE_F(54,64]                                  0.0049569  0.0007282   6.807
## AGE_F(64,74]                                  0.0057453  0.0007388   7.777
## AGE_F(74,100]                                 0.0084310  0.0007270  11.598
## SEX_FFemale                                  -0.0085557  0.0005312 -16.106
## RACE_FBlack                                   0.0223772  0.0033314   6.717
## RACE_FOther/Unk                              -0.0016802  0.0020669  -0.813
## RACE_FAsian                                   0.0237505  0.0050021   4.748
## INCOME_F$38,000 - $47,999                    -0.0010712  0.0010236  -1.046
## INCOME_F$48,000 - $62,999                    -0.0028864  0.0010698  -2.698
## INCOME_F$63,000 +                            -0.0063578  0.0011735  -5.418
## U_R_FUrban                                   -0.0022755  0.0008187  -2.780
## U_R_FRural                                    0.0018322  0.0019826   0.924
## FACILITY_TYPE_FComprehensive Comm Ca Program -0.0064353  0.0011296  -5.697
## FACILITY_TYPE_FAcademic/Research Program     -0.0098655  0.0011124  -8.869
## FACILITY_TYPE_FIntegrated Network Ca Program -0.0025561  0.0012934  -1.976
## FACILITY_LOCATION_FMiddle Atlantic            0.0017956  0.0012371   1.451
## FACILITY_LOCATION_FSouth Atlantic             0.0049151  0.0012196   4.030
## FACILITY_LOCATION_FEast North Central         0.0045108  0.0012441   3.626
## FACILITY_LOCATION_FEast South Central         0.0053204  0.0015035   3.539
## FACILITY_LOCATION_FWest North Central         0.0015550  0.0013967   1.113
## FACILITY_LOCATION_FWest South Central         0.0146708  0.0015962   9.191
## FACILITY_LOCATION_FMountain                   0.0084461  0.0015581   5.421
## FACILITY_LOCATION_FPacific                   -0.0015244  0.0013144  -1.160
## EDUCATION_F13 - 20.9%                        -0.0004141  0.0010313  -0.402
## EDUCATION_F7 - 12.9%                         -0.0032919  0.0010757  -3.060
## EDUCATION_FLess than 7%                      -0.0051212  0.0011914  -4.299
## EXPN_GROUPPre-Expansion                      -0.0224662  0.0007473 -30.063
##                                              Pr(>|t|)    
## (Intercept)                                   < 2e-16 ***
## AGE_F(54,64]                                 1.00e-11 ***
## AGE_F(64,74]                                 7.48e-15 ***
## AGE_F(74,100]                                 < 2e-16 ***
## SEX_FFemale                                   < 2e-16 ***
## RACE_FBlack                                  1.86e-11 ***
## RACE_FOther/Unk                              0.416276    
## RACE_FAsian                                  2.05e-06 ***
## INCOME_F$38,000 - $47,999                    0.295348    
## INCOME_F$48,000 - $62,999                    0.006975 ** 
## INCOME_F$63,000 +                            6.04e-08 ***
## U_R_FUrban                                   0.005444 ** 
## U_R_FRural                                   0.355399    
## FACILITY_TYPE_FComprehensive Comm Ca Program 1.22e-08 ***
## FACILITY_TYPE_FAcademic/Research Program      < 2e-16 ***
## FACILITY_TYPE_FIntegrated Network Ca Program 0.048125 *  
## FACILITY_LOCATION_FMiddle Atlantic           0.146646    
## FACILITY_LOCATION_FSouth Atlantic            5.58e-05 ***
## FACILITY_LOCATION_FEast North Central        0.000288 ***
## FACILITY_LOCATION_FEast South Central        0.000402 ***
## FACILITY_LOCATION_FWest North Central        0.265582    
## FACILITY_LOCATION_FWest South Central         < 2e-16 ***
## FACILITY_LOCATION_FMountain                  5.94e-08 ***
## FACILITY_LOCATION_FPacific                   0.246137    
## EDUCATION_F13 - 20.9%                        0.688029    
## EDUCATION_F7 - 12.9%                         0.002212 ** 
## EDUCATION_FLess than 7%                      1.72e-05 ***
## EXPN_GROUPPre-Expansion                       < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.02156817)
## 
##     Null deviance: 7052.0  on 325127  degrees of freedom
## Residual deviance: 7011.8  on 325100  degrees of freedom
##   (10945 observations deleted due to missingness)
## AIC: -324662
## 
## Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
##                                              Odds ratio     2.5 %
## (Intercept)                                   1.0508079 2.4658347
## AGE_F(54,64]                                  1.0049692 0.9907252
## AGE_F(64,74]                                  1.0057619 0.9897459
## AGE_F(74,100]                                 1.0084667 0.9780726
## SEX_FFemale                                   0.9914808 1.0094131
## RACE_FBlack                                   1.0226295 0.9023407
## RACE_FOther/Unk                               0.9983212 0.9933638
## RACE_FAsian                                   1.0240347 0.9357581
## INCOME_F$38,000 - $47,999                     0.9989294 1.0011879
## INCOME_F$48,000 - $62,999                     0.9971178 1.0054382
## INCOME_F$63,000 +                             0.9936624 1.0076277
## U_R_FUrban                                    0.9977271 1.0027208
## U_R_FRural                                    1.0018339 0.9968215
## FACILITY_TYPE_FComprehensive Comm Ca Program  0.9935853 1.0216563
## FACILITY_TYPE_FAcademic/Research Program      0.9901830 1.0366892
## FACILITY_TYPE_FIntegrated Network Ca Program  0.9974472 1.0219238
## FACILITY_LOCATION_FMiddle Atlantic            1.0017972 0.9930353
## FACILITY_LOCATION_FSouth Atlantic             1.0049272 0.9905551
## FACILITY_LOCATION_FEast North Central         1.0045209 0.9910876
## FACILITY_LOCATION_FEast South Central         1.0053346 0.9921076
## FACILITY_LOCATION_FWest North Central         1.0015562 0.9866529
## FACILITY_LOCATION_FWest South Central         1.0147790 0.9704864
## FACILITY_LOCATION_FMountain                   1.0084818 0.9835266
## FACILITY_LOCATION_FPacific                    0.9984767 0.9864458
## EDUCATION_F13 - 20.9%                         0.9995860 1.0037594
## EDUCATION_F7 - 12.9%                          0.9967135 1.0083182
## EDUCATION_FLess than 7%                       0.9948919 1.0129062
## EXPN_GROUPPre-Expansion                       0.9777843 1.0064959
##                                                 97.5 %
## (Intercept)                                  2.4936548
## AGE_F(54,64]                                 0.9949229
## AGE_F(64,74]                                 0.9939973
## AGE_F(74,100]                                0.9822007
## SEX_FFemale                                  1.0125237
## RACE_FBlack                                  0.9201994
## RACE_FOther/Unk                              1.0053174
## RACE_FAsian                                  0.9633928
## INCOME_F$38,000 - $47,999                    1.0071707
## INCOME_F$48,000 - $62,999                    1.0117137
## INCOME_F$63,000 +                            1.0145225
## U_R_FUrban                                   1.0075010
## U_R_FRural                                   1.0083443
## FACILITY_TYPE_FComprehensive Comm Ca Program 1.0283628
## FACILITY_TYPE_FAcademic/Research Program     1.0433908
## FACILITY_TYPE_FIntegrated Network Ca Program 1.0296261
## FACILITY_LOCATION_FMiddle Atlantic           1.0001676
## FACILITY_LOCATION_FSouth Atlantic            0.9975764
## FACILITY_LOCATION_FEast North Central        0.9982476
## FACILITY_LOCATION_FEast South Central        1.0008152
## FACILITY_LOCATION_FWest North Central        0.9946588
## FACILITY_LOCATION_FWest South Central        0.9795270
## FACILITY_LOCATION_FMountain                  0.9924568
## FACILITY_LOCATION_FPacific                   0.9939737
## EDUCATION_F13 - 20.9%                        1.0098061
## EDUCATION_F7 - 12.9%                         1.0146482
## EDUCATION_FLess than 7%                      1.0199438
## EXPN_GROUPPre-Expansion                      1.0108502